WO2021051281A1

WO2021051281A1 - Point-cloud noise filtering method, distance measurement device, system, storage medium, and mobile platform

Info

Publication number: WO2021051281A1
Application number: PCT/CN2019/106237
Authority: WO
Inventors: 吴特思; 王闯; 陈涵
Original assignee: 深圳市大疆创新科技有限公司
Priority date: 2019-09-17
Filing date: 2019-09-17
Publication date: 2021-03-25
Also published as: CN112912756A

Abstract

Provided are a point-cloud noise filtering method, distance measurement device, system, storage medium, and mobile platform, the method comprising: obtaining feature information of each point-cloud point in point cloud data collected by a ranging device within a preset continuous time window (S501), the feature information comprising at least one of the following: depth, scan angle, spatial curvature, pulse waveform features, reflectivity, and echo energy; according to whether the change of the feature information of the continuous point-cloud points within the preset continuous time window is continuous, determining the noise points in the point cloud data collected within the preset continuous time window (S502), the noise points comprising at least one point-cloud point that is not continuous with the feature information of other point-cloud points within the preset continuous time window. The timing information and original sampling data inherent to the point-cloud points in the point cloud data collected by the ranging device is fully utilized, can effectively filter light noise points, rain and fog noise points, crosstalk noise points, and wire-drawing noise points, etc., significantly improving the quality of the point cloud.

Description

Point cloud noise filtering method, distance measuring device, system, storage medium and mobile platform

Manual

Technical field

The present invention generally relates to the technical field of distance measuring devices, and more specifically to a method for filtering noise of a point cloud, a distance measuring device, a system, a storage medium and a mobile platform.

Background technique

Continuous single-point ranging to form a spatial three-dimensional point cloud perceptual measurement equipment in actual use, due to its structure, optics, hardware design limitations and the influence of the external complex environment, there may be a large deviation in the calculation results of a single ranging , And finally form noise in the 3D point cloud.

Lidar is a typical sensing measurement device that forms a spatial three-dimensional point cloud by continuous single-point ranging. The point cloud noise problem has always been one of the core issues to be solved in this field. Generally speaking, there are two main reasons for Lidar noise: (1) The radar receives some light pulse signals that are not reflected by the measurement target (such as sunlight, background light noise, rain, snow and dust, other radars). Crosstalk, etc.), but due to the limitations of optics, hardware or algorithm design, it is mistakenly regarded as a normal echo signal for calculation, resulting in noise; (2) The normal pulse echo reflected by the measurement target object is distorted in the analog circuit , Causing the solution deviation of the depth calculation model. The above two kinds of noises are difficult to directly identify and filter through the characteristics of their sampled signals.

Therefore, in view of the above-mentioned problems, the present invention proposes a method for filtering noise of a point cloud of a distance measuring device, a distance measuring device, a system, a storage medium and a mobile platform.

Summary of the invention

The present invention is proposed in order to solve at least one of the above-mentioned problems. Specifically, one aspect of the present invention provides a method for point cloud noise filtering of a distance measuring device, the method including:

Obtain feature information of each point cloud point in the point cloud data collected by the distance measuring device within a preset continuous time window, where the feature information includes at least one of the following: depth, scan angle, spatial curvature, pulse waveform Characteristics, reflectivity, echo energy;

Determine the noise in the point cloud data collected in the preset continuous time window according to whether the change of the characteristic information of the continuous point cloud points in the preset continuous time window is continuous, wherein the noise includes At least one point cloud point that is not continuous with the feature information of other point cloud points within the preset continuous time window.

Another aspect of the present invention provides a distance measuring device. The distance measuring device includes one or more processors that work together or separately, and the processors are used to:

Another aspect of the present invention provides a distance measurement system, the distance measurement system includes a distance measurement device and a display unit, wherein one of the distance measurement device and the display unit is used to implement the aforementioned point cloud noise filtering method .

Another aspect of the present invention provides a computer storage medium on which a computer program is stored, and when the program is executed by a processor, the method for filtering noise of the point cloud of the aforementioned distance measuring device is realized.

Another aspect of the present invention provides a mobile platform, which includes:

The aforementioned distance measuring device; and

The platform body, the distance measuring device is installed on the platform body.

The method, distance measuring device and system of the embodiments of the present invention break through the limitation of single pulse sampling, make full use of the inherent timing information and original sampling data of the point cloud points in the point cloud data collected by the distance measuring device, and can effectively filter light noise, Rain and fog noise, crosstalk noise, and wire drawing noise, etc., significantly reduce the abnormal noise in the point cloud data output by the ranging device, and significantly optimize and improve the quality of the point cloud; moreover, the method of the embodiment of the present invention only requires less The amount of calculation can be realized, the processing result has no delay and high accuracy, and the process of noise filtering is almost completed in real time with the point cloud collection.

Description of the drawings

In order to explain the technical solutions in the embodiments of the present invention more clearly, the following will briefly introduce the drawings needed in the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained from these drawings without creative labor.

Figure 1 shows a schematic diagram of noise caused by direct sunlight entering the lidar in an embodiment of the present invention;

Figure 2 shows a schematic diagram of the principle of generating multiple echoes in a laser ranging system in an embodiment of the present invention;

Fig. 3 shows a schematic diagram of the generation principle of multi-echo fusion noise of the laser ranging system in an embodiment of the present invention;

FIG. 4 shows a schematic diagram of wire drawing noise in a point cloud in an embodiment of the present invention;

FIG. 5 shows a schematic flowchart of a point cloud noise filtering method of a distance measuring device in an embodiment of the present invention;

Fig. 6 shows a schematic diagram of the scanning track of the dual-prism lidar in an embodiment of the present invention;

FIG. 7 shows a schematic structural diagram of a distance measuring device in an embodiment of the present invention;

Fig. 8 shows a schematic diagram of a distance measuring device in an embodiment of the present invention;

Fig. 9 shows a schematic block diagram of a ranging system in an embodiment of the present invention.

detailed description

In order to make the objectives, technical solutions, and advantages of the present invention more obvious, the exemplary embodiments according to the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments of the present invention, and it should be understood that the present invention is not limited by the exemplary embodiments described herein. Based on the embodiments of the present invention described in the present invention, all other embodiments obtained by those skilled in the art without creative work should fall within the protection scope of the present invention.

In the following description, a lot of specific details are given in order to provide a more thorough understanding of the present invention. However, it is obvious to those skilled in the art that the present invention can be implemented without one or more of these details. In other examples, in order to avoid confusion with the present invention, some technical features known in the art are not described.

It should be understood that the present invention can be implemented in different forms and should not be construed as being limited to the embodiments presented here. On the contrary, the provision of these embodiments will make the disclosure thorough and complete, and will fully convey the scope of the present invention to those skilled in the art.

The purpose of the terms used here is only to describe specific embodiments and not as a limitation of the present invention. When used herein, the singular forms "a", "an" and "the/the" are also intended to include plural forms, unless the context clearly indicates otherwise. It should also be understood that the terms "composition" and/or "including", when used in this specification, determine the existence of the described features, integers, steps, operations, elements and/or components, but do not exclude one or more other The existence or addition of features, integers, steps, operations, elements, components, and/or groups. As used herein, the term "and/or" includes any and all combinations of related listed items.

In order to thoroughly understand the present invention, a detailed structure will be proposed in the following description to explain the technical solution proposed by the present invention. The optional embodiments of the present invention are described in detail as follows. However, in addition to these detailed descriptions, the present invention may also have other embodiments.

The ranging principle of the laser ranging system is to actively emit laser pulses to the detected object, receive the laser echo signal, and calculate the distance of the object to be measured according to the time difference between laser emission and reception, and obtain based on the known emission direction of the laser Angle information of the measured object. Through high-frequency transmission and reception, the distance and angle information of massive detection points are obtained, and a point cloud is formed to realize the three-dimensional reconstruction of the surrounding scene.

Generally speaking, there are two main reasons for lidar noise:

1. The radar receives some light pulse signals that are not reflected by the measurement target (such as sunlight, background light noise, rain, snow and dust, crosstalk from other radars, etc.), but due to optical, hardware or algorithm design limitations, errors Treat it as a normal echo signal for calculation, resulting in noise. The above-mentioned noise can be divided into three categories: (a) the signal formed by sunlight or its diffuse reflection, referred to as light noise for short, such as the noise in the rectangular frame as shown in Figure 1; (b) the tiny particles such as rain, fog, snow and dust scattered in the air Particles, referred to as rain and fog noise; (c) Crosstalk of other Lidar signals working in the same wavelength range, referred to as crosstalk noise.

2. The normal pulse echo reflected by the measuring target object is distorted in the analog circuit, which causes the solution deviation of the depth calculation model. The most common situation is that there are two or more objects very close to each other in the target direction, as shown in Figure 2. The laser emitted by lidar usually has a certain divergence angle θ, which means that from the launch point, the area of the spot will increase with the increase of distance. Suppose the distance d from the emission point _1, of the laser spot area S _d1, since the actual complexity of the environment, all of the spot S _d1 may fall either on an object, it may fall on only a portion of an object A, the other part It falls on _{another object B with a distance of d 2} (in a complex scene, it may fall on several different objects in a row). Each part of the light spot will form an echo and be received by the system ranging module, causing the receiving module to continuously receive two or more echoes in a very short time. Due to the limitation of the bandwidth of the analog circuit, the above multiple echoes will be merged into one echo. Wave, as shown in Figure 3. After the calculation of the algorithm model, the point cloud usually appears "drawing" between adjacent objects, so this type of noise is called drawing noise, as shown in Figure 4.

Because the speed of light is constant in a certain environment, if the distance between d1 and d2 is far, the receiving system will obtain two independent laser echoes and process them separately for correct calculation; if the distance between d1 and d2 is relatively short, the two times The waves are fused but the system can still distinguish between the two echoes. The receiving system will switch the matching calculation model to perform the correct calculation; but when the distance between d1 and d2 is less than the limit distance dmin, the fusion of the two laser echoes cannot After being analyzed by the system, it will be mistakenly regarded as a single echo for wrong calculation, as shown in Figure 3, forming noise.

Existing noise filtering methods can generally be divided into product bottom-layer noise filtering schemes and upper-layer algorithm noise filtering schemes. The limitations of the existing schemes are summarized as follows:

1. The noise filtering scheme at the bottom of the product is usually judged based on single pulse sampling information, and can only filter out specific noise (such as rain, snow and dust) to a limited extent, and the noise mentioned in the previous article is difficult to extract from the single pulse sampling information Screening is performed, so the noise filtering effect of this scheme is not ideal.

2. The upper-level algorithm noise filtering scheme is usually based on the spatial coordinate relationship between each point in the point cloud and its neighboring points, and processes the entire frame of point cloud after a long time accumulation. Moreover, due to bandwidth limitations, upper-level algorithms can usually only obtain limited information such as the xyz coordinates and reflection intensity of the point cloud, while the timing relationship between the radar scan points and the original sampling data information of the pulse signal will be correspondingly lost. The above factors lead to the following shortcomings in the noise filtering scheme: 1) It can only filter out sparse points in the space. However, the two kinds of noise mentioned in the previous article often appear as agglomeration or continuous state, so the effect of this noise filtering strategy is also very limited. 2) The calculation of noise filtering through spatial relationships usually requires extremely high point cloud density, otherwise it is difficult to accurately identify sparse noise points. However, most practical application scenarios do not have enough time to accumulate high-density point clouds, which makes the upper-layer algorithm often more effective in filtering noise. difference. 3) It needs to accumulate a certain amount of time before processing, and the amount of three-dimensional calculation is relatively large, which results in a relatively high output delay. (4) The computing power requirements are relatively high, and a dedicated high-performance computing platform is required, which is difficult to implement in the embedded firmware of the sensor.

In view of the foregoing problems, an embodiment of the present invention provides a method for filtering noise from a point cloud of a distance measuring device. As shown in FIG. 5, the method includes the following steps: Suppose the feature information of each point cloud point in the point cloud data collected in a continuous time window, the feature information includes at least one of the following: depth, scan angle, spatial curvature, pulse waveform characteristics, reflectivity, echo energy In step S502, determine the noise points in the point cloud data collected in the preset continuous time window according to whether the change in the characteristic information of the continuous point cloud points within the preset continuous time window is continuous, Wherein, the noise point includes at least one point cloud point that is not continuous with feature information of other point cloud points within the preset continuous time window. Through the above method, the characteristic information of each point cloud point in the point cloud data collected by the distance measuring device in the preset continuous time window is acquired, and the characteristic information of the continuous point cloud points in the preset continuous time window is obtained. Whether the change is continuous or not, the noise in the point cloud data collected within the preset continuous time window is determined, so as to identify and filter the noise in the point cloud data. Therefore, the method of the embodiment of the present invention has the following advantages: 1 ) This method breaks through the limitation of single pulse sampling, makes full use of the inherent timing information and original sampling data of the point cloud points in the point cloud data collected by the ranging device, and can effectively filter the light noise, rain and fog noise, and rain and fog noise that cannot be identified by existing solutions. Crosstalk noise and wire drawing noise, etc. 2) The embodiment of the present invention is implemented in the bottom layer firmware of the product, which can use the original sampled data to more accurately distinguish between noise and non-noise. In actual use, the probability of misjudgment and missed judgment is far less than the existing upper layer based on the spatial coordinate relationship. Algorithmic noise filtering scheme. 3) The present invention implements the noise filtering function in the embedded bottom firmware based on the sampling information in the continuous time window, so it only needs to maintain a data buffer of several KB in the firmware to realize the data storage and calculation, and the system overhead It is small and has strong platform adaptability. It can achieve point cloud data output with almost no delay while achieving noise filtering.

Hereinafter, the method for filtering noise of the point cloud of the distance measuring device of the present application will be described in detail with reference to the drawings. In the case of no conflict, the following embodiments and features in the implementation can be combined with each other.

First, as shown in FIG. 5, in step S501, feature information of each point cloud point in the point cloud data collected by the distance measuring device within a preset continuous time window is acquired, and the feature information includes at least one of the following Species: depth, scan angle, spatial curvature, pulse waveform characteristics, reflectivity, echo energy, by acquiring the characteristic information of each point cloud point in the point cloud data collected within a preset continuous time window, the It is used to identify and filter the noise in the point cloud data in the underlying firmware of the ranging device, thus breaking through the limitation of single pulse sampling and making full use of the inherent point cloud points in the point cloud data collected by the ranging device The timing information and original sampling data can effectively filter the light noise, rain and fog noise, crosstalk noise and drawing noise.

Since the distance measuring device such as lidar has the characteristic of "continuous high-frequency scanning according to a specific scanning mode", the scene is continuously scanned according to a specific trajectory, so each point cloud point (also called a detection point) has obvious Timing and location information. When the points in any time window are mapped to the scan trajectory, they will form a continuous line segment, as shown in Figure 6 is a typical double-prism lidar scan trajectory.

The characteristic information of each point cloud point can be acquired based on any suitable method. For example, acquiring the depth of the point cloud point collected by the distance measuring device specifically includes: transmitting a light pulse sequence; converting the received back light reflected by the object It is an electrical signal output; the output of the electrical signal is sampled to measure the time difference between the transmission and reception of the light pulse sequence; the time difference is received, and the depth value of the current point cloud point is calculated. This method can obtain the depth values of all the point cloud points collected by the distance measuring device. In one example, the detector of the distance measuring device may receive the return light reflected by the object. Since the transmitted light pulse sequence is the pulse waveform reflected by the object, the characteristics of the pulse waveform include pulse waveform pulses. Broad features, pulse height, pulse area, etc., or other pulse shape features may also be included. In other examples, the hardware circuit of the distance measuring device may also be provided with a unit for detecting echo energy to obtain the echo energy of each point cloud point. For the acquisition of the reflectivity, for example, the reflectivity can be estimated based on the intensity of the echo energy, or the reflectivity of each point cloud point can be calculated based on other data processing methods well known to those skilled in the art.

Next, continue as shown in FIG. 5, in step S502, according to whether the change of the characteristic information of the continuous point cloud points in the preset continuous time window is continuous, it is determined all the data collected in the preset continuous time window. The noise in the point cloud data, wherein the noise includes at least one point cloud point that is discontinuous with the feature information of other point cloud points within the preset continuous time window, and the feature information changes continuously to identify The point cloud point with a sudden change in the feature information is then determined whether the point cloud point with a sudden change is a noise point, and the noise point in the point cloud data is filtered out. Optionally, in the point cloud data, one of the point cloud points is confirmed as a noise point before the other point cloud point is collected. Therefore, the processing result has no delay and high accuracy, and the noise filtering process is almost With the point cloud collection done in real time.

In an example, the point cloud noise filtering method of the embodiment of the present invention implements the noise filtering function in the embedded bottom firmware of the distance measuring device based on the sampling information in the continuous time window, and the embedded bottom firmware includes a data buffer. To store the characteristic information of the point cloud points collected in the continuous time window, the characteristic information includes at least one of the aforementioned depth, scan angle, spatial curvature, pulse waveform characteristics, reflectivity, echo energy, etc. Species or other feature information of point cloud points that can be used for noise filtering. The determining the noise in the point cloud data collected in the preset continuous time window according to whether the change of the characteristic information of the continuous point cloud points in the preset continuous time window is continuous includes: The feature information of the point cloud points collected in the continuous time window is extracted from the data buffer, and the noise points in the point cloud data collected in the preset continuous time window are determined. Therefore, the point cloud noise filtering method of the present invention can realize data storage and calculation only by maintaining a small data buffer (such as a data buffer with a size of several KB) in the firmware, with low system overhead and platform adaptation. Strong performance, can achieve almost no delay point cloud data output while achieving noise filtering.

In an example, determining the noise points in the point cloud data collected within the preset continuous time window according to whether the characteristic information of the continuous point cloud points within the preset continuous time window changes continuously, including : Determine the isolation degree of the feature information of the current point cloud point, where the isolation degree is used to determine whether the change in the feature information of the current point cloud point is at least within the pre-order time window before the current point cloud point One point cloud point and/or at least one point cloud point in the subsequent subsequent time window are continuous; according to the isolation degree of the feature information of the current point cloud point, it is determined whether the current point cloud point is a noise point, for example , Determine the isolation degree of the feature information of the current point cloud point based on a preset mapping function; determine whether the current point cloud point is a noise according to the comparison result of the isolation degree of the feature information and a set threshold, wherein, when When the isolation degree of the feature information is greater than the set threshold, it is determined that the current point cloud point is a noisy point, where the greater the isolation degree, the corresponding feature information of the current point cloud point deviates from the feature information of other point cloud points If it is greater than the set threshold, it can be determined that the current point cloud point is a noise point.

For example, when the feature information includes depth, taking into account the characteristics of high-frequency scanning of distance measuring devices such as lidar (for example, point cloud interval time 10us), if there is no noise in the continuous point cloud data within the preset continuous time window, Then its depth change will generally show the characteristics of continuous and regular gradual change over time. Conversely, if there are some isolated points that are not continuous with most points in the continuous point cloud data, these isolated points may be noise to a large extent. Based on the above change characteristics, the "depth isolation" of a single point cloud point can be defined according to the following formula (1),

Among them, DI _{n is} defined as the "depth isolation" of the nth point cloud point; w _f and w _b are the size of the pre-order and post-order time windows respectively; (d _nw ,d _n-w+1 ...,d _n ...d _n+w-1 ,d _n+w ) is the sequence arrangement of depth values in the time window; f(·) is the preset mapping function. According to the algorithm design, the higher the "depth isolation", the greater the possibility of noise. If the "depth isolation" DI _{n is} greater than the set threshold T, the nth point cloud point is considered to be a noise point.

The preset mapping function can be set reasonably based on the law of depth change to obtain "depth isolation", for example, the preset mapping function is determined based on the difference between the depth value of the current point cloud point and at least one adjacent point cloud point , The adjacent point cloud point is the point cloud point collected before the current point cloud point, even if the pre-order time window w _f takes the value 1, and the post-order time window w _b takes the value 0, the preset The mapping function can be expressed by the following formula (2):

f(d _n-1 ,d _n )=d _n -d _n-1 formula (2)

The cloud point of the n points (e.g., point cloud-point current) depth value D _n and n-1 th point cloud points (e.g., point cloud acquired before the current point adjacent to the point cloud point) the value of the depth D _n-1 Substituting the above formula (2), the obtained value can be regarded as the "depth isolation" of the nth point cloud point, and the "depth isolation" is compared with the set threshold. If the "depth isolation" is greater than all When the threshold is set, it is determined that the current point cloud point is a noise point.

The set threshold can be reasonably determined by the law of depth change and prior experience. For example, the set threshold can be determined based on the scanning angular velocity of the distance measuring device, the maximum filtering angle, and the depth value of the current point cloud point, where The maximum filtering angle is the angle between the advancing direction of the light pulse sequence emitted by the distance measuring device and the reference surface, which can be expressed by the following formula (3) as the set threshold T:

Wherein, [alpha] is a scanning angular speed, the angle [theta] is maximum filtering, _n D point cloud points for the current depth value, i.e., the n-th point cloud points depth value.

The included angle θ between the forward direction of the light pulse sequence emitted by the distance measuring device and the reference surface can be reasonably adjusted according to actual filtering requirements. Optionally, the included angle can range from 0° to 90°, and more Further, the range of the included angle may also be 0-80°, or may also be 0-30°, 0-40°, 0-50°, and so on. In the embodiment of the present invention, denoising effects of different intensities can be obtained by adjusting the included angle θ. When θ approaches 90°, almost all non-normal incidence point clouds will be filtered out according to the solution in the embodiment of the present invention; when θ approaches 0°, almost no point cloud will be filtered according to the solution in the embodiment of the present invention Any point cloud. Adjust θ to get different degrees of filtering of waveform fusion noise or other abnormal noise.

According to different types of distance measuring devices, different set thresholds T can be used. In one example, if the scanning angular velocity of the distance measuring device is uniform within the field of view, then for the predetermined maximum filtering angle, α/sinθ is also a fixed value, and the set threshold T may be the product of the fixed value and the depth value of the current point cloud point. Since the point cloud collected by the ranging device when detecting the target scene includes multiple point cloud points, the current point cloud point can be any one of the multiple point cloud points, and each different current point The cloud point may correspond to a different set threshold T.

In another example, if the scanning angular velocity of the distance measuring device is non-uniform in the field of view, the scanning angular velocity in the entire field of view is a two-dimensional data set related to the scanned zenith angle and azimuth angle , Then for the predetermined included angle, α/sinθ is a two-dimensional data set. A threshold data set is obtained based on the two-dimensional data set, so as to filter the point cloud.

In other examples, if the scanning angular velocity of the distance measuring device is non-uniform in the field of view, the scanning angular velocity in the entire field of view is a two-dimensional data set related to the scanned zenith angle and azimuth angle, Find the maximum scanning angular velocity of the distance measuring device in the two-dimensional data set, the α/sinθ is obtained based on the maximum scanning angular velocity of the distance measuring device and the included angle, and apply the α/sinθ to obtain the threshold value The data set is then applied to filter the current point cloud points. This method has the advantages of saving resources and calculation amount, and can play a predetermined filtering effect on the point cloud.

In addition to determining the noise in the point cloud data collected in the preset continuous time window based on the above-mentioned change characteristics of the depth, the point cloud data collected in the preset continuous time window can also be determined based on the change feature of the scan angle. Noise.

In this article, the scan angle of the current point cloud point is the angle between the space vector of the current point cloud point pointing to the adjacent point cloud point adjacent to the current point cloud point and the space vector of the current point cloud point, where The adjacent point cloud point is the point cloud point collected before the current point cloud point. For example, the scan angle θ _{n of the} current point cloud point n can be calculated based on the following formula,

among them

Is the space vector of point n, that is, the space vector of current point cloud point n,

Is the space vector of the adjacent point cloud point n-1 collected before the current point cloud point n.

For example, the distance measuring device of lidar has the characteristics of high-frequency scanning (for example, the point cloud interval time is roughly 10 us). If there is no noise in a certain continuous point cloud data, its scanning angle should always be at a larger value. Conversely, if the scan angle suddenly drops from a larger value to a minimum value, and then suddenly rises to a larger value again after a limited number of minimum values, it is considered that the above-mentioned sudden changes are several points The cloud points are likely to be noise.

In an example, the feature information further includes a scanning angle, and the points collected within the preset continuous time window are determined according to whether the feature information of the continuous point cloud points within the preset continuous time window changes continuously. The noise in cloud data includes: acquiring the scan angle of each point cloud point in a preset continuous time window, where all point cloud points collected before the first time point in the preset continuous time window The scan angle is above a first threshold scan angle, and the scan angle of at least one point cloud point collected after the first time point is less than the first threshold scan angle and less than the set threshold of the scan angle. The dots are noise. The first threshold scan angle and the set threshold T can be set reasonably according to prior experience and the scanning mode of the distance measuring device, wherein the first threshold scan angle should be greater than the set threshold.

Therefore, the "scan angle mutation degree" An of a single point cloud point (also called a data point) can be defined according to the following formula (4),

Among them, A _{n is} defined as the "scan angle mutation degree" of the nth point cloud point; wf and wb are the pre-order and post-order time window sizes, respectively;

It is the arrangement of the scan angles in the time window according to the time sequence; f(·) is the preset mapping function. According to the algorithm design, the higher the "scan angle mutation degree", the greater the possibility of noise. If the "scanning angle sudden change degree" A _{n is} greater than the set threshold T, the nth point cloud point is considered to be a noise point. The definition of the preset mapping function can be defined in any manner that can reflect the sudden change of the scanning angle, and it is not specifically limited here.

A preset mapping function can be defined by a suitable filtering strategy, and according to the preset mapping function, it is determined whether the characteristic information (such as scanning angle) of the continuous point cloud points within a preset continuous time window changes continuously. For the noise points in the point cloud data collected in the continuous time window, in one example, the preset mapping function is determined by the following filtering strategy, including: the point cloud points collected in the previous time window before the current point cloud point The scan angle, the scan angle of the point cloud point collected in the subsequent time window after the current point cloud point, and the scan angle of the current point cloud point take the minimum scan angle value; according to the minimum scan angle value and the scan angle The comparison result of the set threshold value is determined to determine whether the point cloud point corresponding to the minimum scan angle value is a noise point, wherein, when the minimum scan angle value is less than the set threshold value of the scan angle, it is determined to be the same as the minimum scan angle value. The point cloud point corresponding to the angle value is a noise point. Specifically, the values of the post-order time window and the pre-order time window can be set reasonably according to actual needs. Optionally, when the pre-order time window is 0 and the post-order time window is 1, the minimum scan angle value Is the minimum of the scan angle of the current point cloud point and the scan angle of the adjacent point cloud point adjacent to the current point cloud point; or, the pre-order time window is 1, and the post-order time window If it is 0, the minimum scan angle value is the minimum value of the scan angle of the current point cloud point and the scan angle of the adjacent point cloud point adjacent to the current point cloud point.

The preset mapping function determined by the above filtering strategy can be expressed according to the following formula (5):

f(θ _n ,θ _n+1 )=min(θ _n ,θ _n+1 ) Formula (5)

Among them, take the pre-order time window w _f =0, the post-order time window w _b =1, θ _n is the scan angle of the current point cloud point n, and θ _n+1 is the current point cloud point n’s subsequent time window acquisition The scanning angle of the point cloud point can also be regarded as the adjacent point cloud point collected after the current point cloud point n. Among them, the smaller the scan angle, it means that the scan angle of the corresponding point cloud point has a sudden change compared to the scan angle of other point cloud points, and it is likely to be noise. According to the minimum scan angle value and the The comparison result of the set threshold T of the scan angle determines whether the point cloud point corresponding to the minimum scan angle value is a noise point, wherein, when the minimum scan angle value is less than the set threshold value of the scan angle, it is determined with The point cloud points corresponding to the minimum scan angle value are noise points, and the set threshold T of the scan angle can be set reasonably based on prior experience. For example, the value of T is 2.5°. When the minimum scan angle is less than 2.5°, It is determined that the corresponding point cloud point is a noise point.

It is worth mentioning that, in this article, the preset continuous time window can include the pre-order time window and the post-order time window as well as the collection time of the current point cloud point, which can be passed through the current point cloud point (for example, the nth point cloud point) The acquisition time defines the pre-order time window and the post-order time window, where the pre-order time window is the time window before the acquisition time of the current point cloud point, and the subsequent time window is the time after the current point cloud point acquisition time window. The values of the pre-order time window and the subsequent time window can be set reasonably according to actual needs.

In another example, determining the preset mapping function through the following filtering strategy includes: obtaining the scan angle of each point cloud point within a preset continuous time window containing the collection time of the current point cloud point; The maximum number of point cloud points whose scan angles are less than the set threshold of the scan angle collected in a preset continuous time window, wherein the scan angle of the current point cloud point is less than the set threshold of the scan angle; according to the maximum number and preset The comparison result of the number determines whether the current point cloud point is a noise point, wherein when the maximum number is less than the preset number, it is determined that the current point cloud point is a noise point. According to the above filtering strategy, the preset mapping function can be equal to the maximum number of consecutive scan angles containing the nth point cloud point in the preset continuous time window less than the set threshold T. For example, take the pre-order window size w _f as 10, The sequence time window size w _b is 10, the set threshold T is 10°, and the preset number T _θ is 5, that is, the maximum number of consecutive scan angles containing the nth point cloud point less than the set threshold 10° is less than 5 When, the nth point cloud point is determined as a noise point. By setting the preset number to be used as a filter condition, the noise can be determined more accurately by limiting the number of point cloud points that have abrupt changes. If the preset number is exceeded, it may indicate the scanning of these point cloud points The angle change is continuous, and it is impossible to determine whether it is noise or not.

Because each point cloud point (also called detection point) has obvious timing and location information. Any point in any time window is mapped to the scan trajectory to form a continuous line segment. The curvature of any point cloud point on the scan trajectory is the rotation rate of the tangent direction angle to the arc length for a certain point on the curve. It is defined by differentiation, indicating the degree of deviation of the curve from a straight line, and the value indicating the degree of curvature of the curve at the point cloud point. The greater the curvature, the greater the degree of curvature of the curve. Therefore, the noise can also be determined based on the change characteristics of the spatial curvature.

Also consider the characteristics of high-frequency scanning of distance measuring devices such as lidar. If there is no noise in a certain continuous point cloud data, the curvature change at each continuous point should be similarly close to continuous. Conversely, if there are outliers in the continuous point cloud data, so that the curvature changes drastically at this point, the outliers are likely to be noise.

In one example, the feature information includes spatial curvature, and determining whether the current point cloud point is a noise according to the comparison result of the isolation degree and a set threshold includes: The comparison result of the isolation degree and the set threshold value of spatial curvature determines whether the current point cloud point is a noise point, wherein, when the isolation degree of the spatial curvature of the current point cloud point is greater than the set threshold value, it is determined that the The current point cloud point is noise.

Based on the above change characteristics, the "spatial curvature isolation" of a single point cloud point can be defined by the following formula (6):

Where CI _{n is} defined as the "spatial curvature isolation" of the nth point cloud point; w _f and w _b are the size of the pre-order and post-order time windows respectively; (c _nw ,c _n-w+1 ..., c _n ... c _n+w-1 , c _n+w ) is the arrangement of the curvature values in the time window in time sequence; f(·) is the preset mapping function. According to the algorithm design, the higher the "spatial curvature isolation", the greater the possibility of noise. If the "spatial curvature isolation degree" CI _{n is} greater than the set threshold T, the nth point cloud point is considered to be a noise point.

The noise in the point cloud data can also be determined based on the change characteristics of the pulse waveform. The pulse waveform refers to the pulse waveform of the echo signal received by the ranging device. Among them, the characteristic change of the pulse waveform can mainly act on the multi-echo. Fusion noise filtering, optionally, the characteristics of the pulse waveform include at least one of pulse width, pulse height, and pulse area of the pulse waveform. In this article, the pulse width of the pulse waveform is mainly used as an example for explanation and description, but it is understandable that the noise filtering based on the change characteristics of the pulse waveform is not limited to the pulse width.

Wherein the pulse waveform includes a pulse width of the pulse waveform, the pulse width w t _e OFF time difference of arrival time t _r, wherein the arrival time of the echo signal to trigger the first time to digital converter (TDC) the time of the lowest threshold, and the cut-off time is the time when the echo signal triggers the lowest threshold of the time-to-digital converter (TDC) for the second time.

In the case of multi-echo fusion, the pulse waveform of the echo signal has the following characteristics: 1. The pulse width is obviously broadened; 2. The arrival time of the echo signal is similar to the arrival time of a point completely hitting a nearby object, and it is cut off at the same time The time is close to the cut-off time for hitting a point completely on a distant object.

Based on the above characteristics, the "abnormal spread" of the pulse width of the pulse waveform can be defined. In one example, the abnormal spread of the point cloud points collected in the preset continuous time window can be determined according to a preset mapping function; The noise points in the point cloud data collected within the preset continuous time window are determined according to the abnormal spread width, where the noise points include point cloud points with the abnormal spread width greater than a set threshold.

Specifically, the "abnormal spread" of the pulse width of the pulse waveform can be defined by the following formula (7):

Among them, S _{n is} defined as the "abnormal spread width" of the nth point cloud point; w _f and w _b are the pre-order and post-order time window sizes, respectively;

It is the arrangement of the arrival time and the deadline in the time window according to the time sequence; f(·) is the preset mapping function. According to the algorithm design, the point cloud point with the larger "abnormal spread width" is more likely to be noise. If the "abnormal spread width" S _{n is} greater than the set threshold T, the nth point cloud point is considered to be a noise point.

The preset mapping function of the current point cloud point (for example, the nth point cloud point) is set according to whether the pulse width, arrival time, and cut-off time of the continuous point cloud points of the preset continuous time window meet the preset filter conditions Wherein, when the preset filter condition is met, the abnormal spread of the current point cloud point determined according to the preset mapping function is greater than a set threshold, and when the preset filter condition is not met, according to the The abnormal spread width of the current point cloud point determined by the preset mapping function is less than the set threshold.

In an example, when the pulse width, arrival time, and deadline meet the preset filter conditions, it is determined that the current point cloud point is a noise point, and the preset filter conditions include at least one of the following conditions:

_{The pulse width w t1} of the echo signal at _{the first time point t 1} in the pre-order time window of the current point cloud point has a sudden widening _{compared to the pulse width w t1-1} at the previous time point of the first time point , Wherein the abrupt widening is greater than the first pulse width setting threshold, that is

_{The pulse width w t2} of the echo signal at _{the second time point t 2} in the subsequent time window of the current point cloud point has abruptly reduced _{compared to the pulse width w t2-1} at the previous time point of the second time point , Wherein the sudden change reduction is greater than the first pulse width setting threshold, that is

The arrival time in the time window between the first time point and the second time point is separated from the pre-arrival time of the first time point within a first threshold and the cut-off time in the time window is the same as the first The subsequent cut-off time of the two time points is within the second threshold, that is, the arrival time of the echo signal is close to the arrival time of the point completely hitting the close object, and the cut-off time is the cutoff of the point completely hitting the distant object. The time is similar, or the cut-off time in the time window between the first time point and the second time point is separated from the preceding cut-off time of the first time point within the third threshold time and arrives within the time window The time is separated from the subsequent arrival time of the second time point within a fourth threshold time;

The current point cloud point is within a time window between the first time point and the second time point.

In an example, the pre-order window size w _f can be set to 10, and the post-order time window size w _b is 10; if the arrival time and the deadline in the time window meet the above preset filter conditions, the preset mapping function f(·) The first value can be output, and the first value is the abnormal spread width of the current point cloud point, so that it is greater than the set threshold. When the preset filter condition is not met, the second value can be output according to the preset mapping function. Value, the second value, that is, the abnormal spread of the current point cloud point is less than the set threshold, for example, the first value is 1, the second value is 0, or other suitable values. Among them, if the output of f(·) is 1, the nth point cloud point is considered to be a noise; the output is 0, then the nth point cloud point is considered to be a normal point.

Noise filtering can also be implemented based on the change characteristics of reflectance, especially for multi-echo fusion.

Under the currently applied reflectance calculation model, an abnormally widened waveform will cause the reflectance of the point cloud to be calculated as a very small value. Taking into account the characteristics of high-frequency scanning of distance measuring devices such as lidar (for example, point cloud interval time 10us), if there is no noise in a certain continuous point cloud data, its reflectivity will generally stabilize at a certain larger value. If there are several consecutive points in a time window, the reflectivity suddenly drops from a large value to a minimum value, and then suddenly rises back to a large value after a limited number of minimum values. , It is considered that the above-mentioned consecutive points are noise points.

In an example, the characteristic information includes reflectance, and the characteristic information collected in the preset continuous time window is determined according to whether the change of the characteristic information of the continuous point cloud points in the preset continuous time window is continuous. The noise in the point cloud data includes: the reflectivity of the continuous point cloud points in any time window within the preset continuous time window is reduced from the first reflectivity to the second reflectivity, and the point cloud points are in accordance with the time sequence The reflectivity increases from the second reflectivity to the third reflectivity, wherein the point cloud points with the reflectivity equal to or less than the second reflectivity in the continuous point cloud points are noise points, which means that these continuous point cloud points The point and the change of the reflectivity of the point cloud point of the gas in the preset continuous time window are not continuous, so it is determined that it is a noise point, and the noise point in the point cloud data is identified. Optionally, the second reflectivity can also be less than a set threshold of reflectivity, and the set threshold is reasonably set based on prior experience, where the first reflectivity and the third reflectivity can also be the same reflectivity. , Or it can be a different reflectivity.

Based on the above-mentioned change characteristics of the reflectance, the "abrupt change in reflectance" is defined, where the "abrupt change in reflectance" reflects the degree of change in the reflectance of the point cloud point.

In an example, the characteristic information includes reflectance, and the characteristic information collected in the preset continuous time window is determined according to whether the change of the characteristic information of the continuous point cloud points in the preset continuous time window is continuous. The noise in the point cloud data includes: determining, according to a preset mapping function, the degree of abrupt change in reflectivity of the point cloud points collected within the preset continuous time window; The noise points in the point cloud data collected within a time window, wherein the noise points include point cloud points whose reflectivity abrupt change degree is greater than a set threshold.

The defined "abrupt change in reflectance" can be expressed by the following formula (8):

Among them, R _{n is} defined as the "reflectance mutation degree" of the nth point cloud point; w _f and w _b are the pre-order and post-order time window sizes, respectively;

It is the arrangement of the scan angle in the time window according to the time sequence; f(·) is the mapping function. According to the algorithm design, the larger the "abrupt change in reflectance", the more likely it is noise. If the "abrupt change degree of reflectivity" R _{n is} greater than the set threshold T, the nth point cloud point is considered to be a noise point.

In an example, the preset mapping function of the current point cloud point is set according to whether the reflectivity of the continuous point cloud points of the preset continuous time window meets a preset filter condition, wherein, when the preset filter condition is satisfied When the sudden change in reflectance of the current point cloud point determined according to the preset mapping function is greater than the set threshold, when the preset filtering condition is not met, the current point cloud point determined according to the preset mapping function The abrupt change in reflectance is less than the set threshold.

In an example, the preset continuous time window includes a pre-order time window and a post-order time window, wherein the pre-order time window is located before the acquisition time of the current point cloud point, and the subsequent time window is located in the After the collection time of the current point cloud point, the preset filter conditions include the following conditions:

_{The reflectivity r t1} of the point cloud point collected at the first time point t ₁ of the preamble time window is reduced to below the first threshold reflectivity, and the point cloud points collected before _{the first time point t 1} r reflectance and the reflectance at a first time _t1 collection point of the point cloud points _t1-1 r a difference greater than a set threshold value T _r, for example, a first threshold reflectance value of 3, then r _t1 less than 3, and r _t1-1 -r _t1 is greater than T _r, it indicates that the reflectance at a first time point t1, the point cloud generating steep drop;

_{The reflectivity r t2} of the point cloud points collected at the second time point t ₂ of the subsequent time window is below the first threshold reflectivity, and the point cloud points collected after _{the second time point t 2} reflectance r _{t2 + 1} rises, and the reflectance of acquisition time point after the second point cloud points reflectance r _{t2 + 1} point and cloud point acquired at a second time point _t2, the difference between r greater than the set threshold value T _r; e.g., the first threshold reflectance value of 3, then R & lt _t2 is less than 3, and r _{_t2 + 1} -r _t2 is greater than T _r, at a second time t2 indicates the point cloud points appear The reflectivity rises sharply from below 3, so it indicates that the point cloud points collected between the first time point and the second time point are likely to be noise.

Further, the reflectivity of all the point cloud points collected from the first time point t ₁ to the second time point t ₂ is less than the first threshold reflectivity, and the first time point to the first time point The number of all point cloud points collected within the second time point is less than the threshold number, for example, the reflectivity of all the point cloud points collected from _{the first time point t 1} to the second time point t _{2 is less than 3, and} If the threshold number is set to 10, t ₂ -t _{1 is} less than 10, and through this filter condition, it can be determined that the decrease in reflectivity of the point cloud points collected between the first time point and the second time point is not due to the probe itself Caused by reflectivity. The first threshold reflectivity and the number of thresholds can be set reasonably based on prior experience, and no specific limitation is made here.

Further, if the collection time of the current point cloud point is between the first time point and the second time point, the reflectivity of the current point cloud point is also less than the first threshold reflectivity, so it can be determined that the current point cloud point is Noise.

In an example, the pre-order window size w _f can be taken as 10, and the post-order time window size w _b is 10; if the above-mentioned preset filter condition of reflectivity is satisfied in the time window, the preset mapping function f(·) can be output The first value, the first value, which is the "abrupt change in reflectivity" of the current point cloud point, is greater than the set threshold. When the preset filter condition is not met, the first value can be output according to the preset mapping function. Two values, the second value, that is, the "abrupt change in reflectivity" of the current point cloud point is less than a set threshold, for example, the first value is 1, the second value is 0, or other suitable values. Among them, if the output of f(·) is 1, the nth point cloud point is considered to be a noise; the output is 0, then the nth point cloud point is considered to be a normal point.

The above mainly focuses on the four obvious characteristics of point cloud data in the continuous time window as the basis for filtering noise, which are 1) depth-based change characteristics; 2) scan angle-based change characteristics; 3) reflectance-based change characteristics; 4 ) Based on the pulse waveform change characteristics, the point cloud noise filtering method of the present invention is explained and explained, but it should be understood that:

1. The above four strategies can work independently, or they can be combined arbitrarily to form a composite filtering strategy, that is, they can be filtered through any one, any two, any three, or all four strategies. ；

2. Although the above is only for each strategy of the present invention, only one or two relatively simple and intuitive examples are given. In fact, whether it is the selection of the preset mapping function f(·), the setting of the time window size, the threshold setting of various auxiliary judgments, and the final judgment strategy, there are abundant choices and combinations. Based on the above logic, various filtering strategies can be developed according to actual needs.

3. The filtering strategy can be manually designed through the first law, or it can be used as a feature input model of machine learning, and trained on a labeled data set to obtain a good-performance noise classifier.

In addition to the feature information mentioned above, the point cloud may have other changing features in the time window sequence. For example, if the machine has the ability to accurately detect the echo energy, the change in the echo energy in the time window sequence can also be used as a feature for noise filtering. For example, the noise may have other changes in the time window sequence. Features can be obtained by sampling by additional designed hardware circuits. For example, if the hardware circuit has the ability to accurately detect the echo energy, the change in the echo energy in the time window sequence is also used as a feature for noise filtering. In other examples, in addition to the pulse sampling information in the time window, the input of the noise filtering strategy may also include externally input auxiliary information. For example, whether the current weather is sunny or rainy, such as whether the radar is applied to an indoor environment (which is prone to wire drawing noise) or an outdoor environment, etc., the noise filtering of the point cloud data is assisted by the input of these auxiliary information.

In an example, the point cloud noise filtering method of the embodiment of the present invention is implemented in the underlying firmware of the distance measuring device, such as a field programmable logic gate array FPGA, which can use the original sampling data (such as the data collected by the aforementioned distance measuring device). The feature information of point cloud points) can distinguish noise and non-noise more accurately. In actual use, the probability of misjudgment and miss-judgment is far less than that of the existing upper-level algorithm noise filtering scheme based on spatial coordinate relationship.

The present invention utilizes the inherent "high-frequency scanning according to a specific scanning mode" characteristic of distance measurement devices such as lidars, analyzes and extracts several obvious features of several data points of the above-mentioned noise in a continuous time window as a basis for noise filtering, and formulates The corresponding noise filtering strategy is used to identify and filter the noise. Therefore, the method of the embodiment of the present invention breaks through the limitation of single pulse sampling, makes full use of the inherent timing information and original sampling data of the detection point, and can effectively filter the light noise, rain and fog noise, crosstalk noise, and wiredrawing noise that cannot be identified by existing solutions. .

In an example, the point cloud noise filtering method of the present invention can also be implemented in a display unit, wherein the display unit is communicatively connected with the distance measuring device, and is used to obtain the original sampling data collected by the distance measuring device. Alternatively, the display unit can also be used to display the point cloud data that has been filtered by the distance measuring device or the point cloud data that has not been filtered.

The point cloud points determined as noise by the above method can be processed according to the following method. In one example, when it is determined that the current point cloud point is a noise point, the current point cloud point is filtered out, for example, The current point cloud point determined as a noise point is set to 0, that is, it is directly filtered. Optionally, the point cloud point filtering is performed during the process of collecting point cloud points by the distance measuring device. The distance device can directly output point cloud data with almost no noise. In another example, a point cloud point determined as a noise point is marked, and the marked point cloud point is assigned a special value or the current point cloud point is directly filtered out. The special value is different from other non-point cloud points. The point cloud point of the noise is then processed by the upper-level algorithm (that is, the upper-level application), such as the object segmentation and recognition algorithm, the three-dimensional reconstruction algorithm, and so on. It should be noted that in the embodiment of the present invention, the processed value is only used when sending data to the upper application. In the embodiment of the present invention, the original data before filtering is always retained as a reference for the next filtering algorithm.

Hereinafter, the structure of a distance measuring device in the embodiment of the present invention will be exemplarily described in more detail with reference to FIGS. 7 and 8. The distance measuring device includes a lidar. The distance measuring device is only used as an example. For other suitable measuring devices, Distance devices can also be applied to this application. The distance measuring device is used to implement the point cloud noise filtering method in the foregoing embodiment.

The solutions provided by the various embodiments of the present invention can be applied to a distance measuring device, and the distance measuring device may be electronic equipment such as lidar and laser distance measuring equipment. In one embodiment, the distance measuring device is used to sense external environmental information, for example, distance information, orientation information, reflection intensity information, speed information, etc. of environmental targets. In one implementation, the distance measuring device can detect the distance from the probe to the distance measuring device by measuring the time of light propagation between the distance measuring device and the probe, that is, the time-of-flight (TOF). Alternatively, the ranging device can also detect the distance from the detected object to the ranging device through other technologies, such as a ranging method based on phase shift measurement, or a ranging method based on frequency shift measurement. This is not limited.

For ease of understanding, the working process of distance measurement will be described as an example in conjunction with the distance measurement device 100 shown in FIG. 7.

Exemplarily, the distance measuring device may include a transmitting module, a receiving module, and a temperature control system. The transmitting module is used to emit light pulses; the receiving module is used to receive at least part of the light pulses reflected by the object, and according to The received at least part of the light pulse determines the distance of the object relative to the distance measuring device.

Specifically, as shown in FIG. 7, the transmitting module includes a transmitting circuit 110; the receiving module includes a receiving circuit 120, a sampling circuit 130 and an arithmetic circuit 140.

The transmitting circuit 110 may emit a light pulse sequence (for example, a laser pulse sequence). The receiving circuit 120 can receive the light pulse sequence reflected by the detected object, that is, obtain the pulse waveform of the echo signal through it, and perform photoelectric conversion on the light pulse sequence to obtain the electrical signal, and then the electrical signal can be processed Output to the sampling circuit 130. The sampling circuit 130 may sample the electrical signal to obtain the sampling result. The arithmetic circuit 140 can determine the distance between the distance measuring device 100 and the detected object, that is, the depth, based on the sampling result of the sampling circuit 130.

Optionally, the distance measuring device 100 may further include a control circuit 150 that can control other circuits, for example, can control the working time of each circuit and/or set parameters for each circuit.

It should be understood that although the distance measuring device shown in FIG. 7 includes a transmitting circuit, a receiving circuit, a sampling circuit, and an arithmetic circuit for emitting a light beam for detection, the embodiment of the present application is not limited to this, and the transmitting circuit The number of any one of the receiving circuit, the sampling circuit, and the arithmetic circuit can also be at least two, which are used to emit at least two light beams in the same direction or in different directions; wherein, the at least two light paths can be simultaneous Shooting can also be shooting at different times. In an example, the light-emitting chips in the at least two transmitting circuits are packaged in the same module. For example, each emitting circuit includes a laser emitting chip, and the dies in the laser emitting chips in the at least two emitting circuits are packaged together and housed in the same packaging space.

In some implementations, in addition to the circuit shown in FIG. 7, the distance measuring device 100 may also include a scanning module for changing the propagation direction of at least one light pulse sequence (for example, a laser pulse sequence) emitted by the transmitting circuit, so as to control the field of view. Perform a scan. Exemplarily, the scanning area of the scanning module in the field of view of the distance measuring device increases with the accumulation of time.

Among them, the module including the transmitting circuit 110, the receiving circuit 120, the sampling circuit 130, and the arithmetic circuit 140, or the module including the transmitting circuit 110, the receiving circuit 120, the sampling circuit 130, the arithmetic circuit 140, and the control circuit 150 may be referred to as the measuring circuit. Distance module, the distance measurement module can be independent of other modules, for example, the scanning module.

A coaxial optical path can be used in the distance measuring device, that is, the light beam emitted by the distance measuring device and the reflected light beam share at least part of the optical path in the distance measuring device. For example, after at least one laser pulse sequence emitted by the transmitter circuit changes its propagation direction and exits through the scanning module, the laser pulse sequence reflected by the probe passes through the scanning module and then enters the receiving circuit. Alternatively, the distance measuring device can also adopt an off-axis optical path, that is, the light beam emitted by the distance measuring device and the reflected light beam are transmitted along different optical paths in the distance measuring device. Fig. 8 shows a schematic diagram of an embodiment in which the distance measuring device of the present invention adopts a coaxial optical path.

The ranging device 200 includes a ranging module 210, which includes a transmitter 203 (which may include the above-mentioned transmitting circuit), a collimating element 204, a detector 205 (which may include the above-mentioned receiving circuit, sampling circuit, and arithmetic circuit), and Light path changing element 206. The ranging module 210 is used to emit a light beam, receive the return light, and convert the return light into an electrical signal. Among them, the transmitter 203 can be used to emit a light pulse sequence. In one embodiment, the transmitter 203 may emit a sequence of laser pulses. Optionally, the laser beam emitted by the transmitter 203 is a narrow-bandwidth beam with a wavelength outside the visible light range. The collimating element 204 is arranged on the exit light path of the emitter, and is used to collimate the light beam emitted from the emitter 203, and collimate the light beam emitted from the emitter 203 into parallel light and output to the scanning module. The collimating element is also used to condense at least a part of the return light reflected by the probe. The collimating element 204 may be a collimating lens or other elements capable of collimating a light beam.

In the embodiment shown in FIG. 8, the transmitting light path and the receiving light path in the distance measuring device are combined before the collimating element 204 through the light path changing element 206, so that the transmitting light path and the receiving light path can share the same collimating element, so that the light path More compact. In some other implementation manners, the transmitter 203 and the detector 205 may use their respective collimating elements, and the optical path changing element 206 is arranged on the optical path behind the collimating element.

In the embodiment shown in FIG. 8, since the beam aperture of the light beam emitted by the transmitter 203 is relatively small, and the beam aperture of the return light received by the distance measuring device is relatively large, the light path changing element can use a small area mirror to The transmitting light path and the receiving light path are combined. In some other implementations, the light path changing element may also use a reflector with a through hole, where the through hole is used to transmit the emitted light of the emitter 203 and the reflector is used to reflect the return light to the detector 205. In this way, the shielding of the back light from the support of the small reflector in the case of using the small reflector can be reduced.

In the embodiment shown in FIG. 8, the optical path changing element deviates from the optical axis of the collimating element 204. In some other implementation manners, the optical path changing element may also be located on the optical axis of the collimating element 204.

The distance measuring device 200 further includes a scanning module 202. The scanning module 202 is placed on the exit light path of the distance measuring module 210. The scanning module 202 is used to change the transmission direction of the collimated beam 219 emitted by the collimating element 204 and project it to the external environment, and project the return light to the collimating element 204 . The returned light is collected on the detector 205 via the collimating element 204.

In an embodiment, the scanning module 202 may include at least one optical element for changing the propagation path of the light beam, wherein the optical element may change the propagation path of the light beam by reflecting, refraction, or diffracting the light beam, for example, The optical element includes at least one light refraction element having a non-parallel exit surface and an entrance surface. For example, the scanning module 202 includes a lens, a mirror, a prism, a galvanometer, a grating, a liquid crystal, an optical phased array (Optical Phased Array), or any combination of the foregoing optical elements. In an example, at least part of the optical element is moving, for example, the at least part of the optical element is driven to move by a driving module, and the moving optical element can reflect, refract, or diffract the light beam to different directions at different times. In some embodiments, the multiple optical elements of the scanning module 202 can rotate or vibrate around a common axis 209, and each rotating or vibrating optical element is used to continuously change the propagation direction of the incident light beam. In one embodiment, the multiple optical elements of the scanning module 202 may rotate at different speeds or vibrate at different speeds. In another embodiment, at least part of the optical elements of the scanning module 202 may rotate at substantially the same rotation speed. In some embodiments, the multiple optical elements of the scanning module may also rotate around different axes. In some embodiments, the multiple optical elements of the scanning module may also rotate in the same direction or in different directions; or vibrate in the same direction, or vibrate in different directions, which is not limited herein.

In one embodiment, the scanning module 202 includes a first optical element 214 and a driver 216 connected to the first optical element 214. The driver 216 is used to drive the first optical element 214 to rotate around the rotation axis 209 to change the first optical element 214. The direction of the beam 219 is collimated. The first optical element 214 projects the collimated beam 219 to different directions. In one embodiment, the angle between the direction of the collimated beam 219 changed by the first optical element and the rotation axis 209 changes with the rotation of the first optical element 214. In one embodiment, the first optical element 214 includes a pair of opposing non-parallel surfaces through which the collimated light beam 219 passes. In one embodiment, the first optical element 214 includes a prism whose thickness varies along at least one radial direction. In one embodiment, the first optical element 214 includes a wedge angle prism, and the collimated beam 219 is refracted.

In one embodiment, the scanning module 202 further includes a second optical element 215, the second optical element 215 rotates around the rotation axis 209, and the rotation speed of the second optical element 215 is different from the rotation speed of the first optical element 214. The second optical element 215 is used to change the direction of the light beam projected by the first optical element 214. In one embodiment, the second optical element 215 is connected to another driver 217, and the driver 217 drives the second optical element 215 to rotate. The first optical element 214 and the second optical element 215 can be driven by the same or different drivers, so that the rotation speed and/or rotation of the first optical element 214 and the second optical element 215 are different, so that the collimated light beam 219 is projected to the outside space. Different directions can scan a larger space. In one embodiment, the controller 218 controls the

drivers

216 and 217 to drive the first optical element 214 and the second optical element 215, respectively. The rotational speeds of the first optical element 214 and the second optical element 215 can be determined according to the expected scanning area and pattern in actual applications. The

drivers

216 and 217 may include motors or other drivers.

In one embodiment, the second optical element 215 includes a pair of opposite non-parallel surfaces through which the light beam passes. In one embodiment, the second optical element 215 includes a prism whose thickness varies in at least one radial direction. In one embodiment, the second optical element 215 includes a wedge prism.

In an embodiment, the scanning module 202 further includes a third optical element (not shown) and a driver for driving the third optical element to move. Optionally, the third optical element includes a pair of opposite non-parallel surfaces, and the light beam passes through the pair of surfaces. In one embodiment, the third optical element includes a prism whose thickness varies in at least one radial direction. In one embodiment, the third optical element includes a wedge prism. At least two of the first, second, and third optical elements rotate at different rotation speeds and/or rotation directions.

In an embodiment, the scanning module includes two or three light refraction elements arranged in sequence on the exit light path of the light pulse sequence. Optionally, at least two of the light refraction elements in the scanning module rotate during the scanning process to change the direction of the light pulse sequence.

The scanning path of the scanning module is different at least partly at different moments. The rotation of each optical element in the scanning module 202 can project light to different directions, such as the direction of the projected light 211 and the direction 213. Space to scan. When the light 211 projected by the scanning module 202 hits the detection object 201, a part of the light is reflected by the detection object 201 to the distance measuring device 200 in a direction opposite to the projected light 211. The return light 212 reflected by the probe 201 is incident on the collimating element 204 after passing through the scanning module 202.

The detector 205 and the transmitter 203 are placed on the same side of the collimating element 204, and the detector 205 is used to convert at least part of the return light passing through the collimating element 204 into electrical signals.

In one embodiment, an anti-reflection film is plated on each optical element. Optionally, the thickness of the antireflection film is equal to or close to the wavelength of the light beam emitted by the emitter 203, which can increase the intensity of the transmitted light beam.

In one embodiment, a filter layer is plated on the surface of an element located on the beam propagation path in the distance measuring device, or a filter is provided on the beam propagation path for transmitting at least the wavelength band of the beam emitted by the transmitter, Reflect other bands to reduce the noise caused by ambient light to the receiver.

In some embodiments, the transmitter 203 may include a laser diode through which nanosecond laser pulses are emitted. Further, the laser pulse receiving time can be determined, for example, the laser pulse receiving time can be determined by detecting the rising edge time and/or the falling edge time of the electrical signal pulse. In this way, the distance measuring device 200 can calculate the TOF by using the pulse receiving time information and the pulse sending time information, so as to determine the distance between the probe 201 and the distance measuring device 200. The distance and azimuth detected by the distance measuring device 200 can be used for remote sensing, obstacle avoidance, surveying and mapping, modeling, navigation, and the like.

In some embodiments, the distance measuring device further includes one or more processors, one or more storage devices, and one or more processors work together or individually. Optionally, the distance measuring device may further include at least one of an input device (not shown), an output device (not shown), and an image sensor (not shown), and these components are connected through a bus system and/or other forms The mechanisms (not shown) are interconnected.

The storage device, that is, the memory used for storing processor-executable instructions, for example, is used for the existence of corresponding steps and program instructions in the method for implementing the point cloud noise filtering of the distance measuring device according to the embodiment of the present invention. It may include one or more computer program products, and the computer program products may include various forms of computer-readable storage media, such as volatile memory and/or non-volatile memory. The volatile memory may include random access memory (RAM) and/or cache memory (cache), for example. The non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, and the like.

The input device may be a device used by a user to input instructions, and may include one or more of a keyboard, a mouse, a microphone, and a touch screen.

The output device may output various information (such as images or sounds) to the outside (such as a user), and may include one or more of a display, a speaker, etc., for collecting non-noise points collected by the distance measuring device. The cloud point is output as an image or video.

The communication interface (not shown) is used for communication between the ranging device and other devices, including wired or wireless communication. The ranging device can access a wireless network based on a communication standard, such as WiFi, 2G, 3G, 4G, 5G, or a combination thereof. In an exemplary embodiment, the communication interface receives a broadcast signal or broadcast related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication interface further includes a near field communication (NFC) module to facilitate short-range communication. For example, the NFC module can be implemented based on radio frequency identification (RFID) technology, infrared data association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology and other technologies.

The processor may be a central processing unit (CPU), an image processing unit (GPU), an application specific integrated circuit (ASIC), a field programmable logic gate array (FPGA), or other forms with data processing capabilities and/or instruction execution capabilities The processing unit, and can control other components in the ranging device to perform desired functions. The processor can execute the instructions stored in the storage device to execute the method for filtering noise from a point cloud of a distance measuring device described herein. For the method for filtering noise, refer to the description in the foregoing embodiment, and will not be repeated here. Go into details. For example, the processor can include one or more embedded processors, processor cores, microprocessors, logic circuits, hardware finite state machines (FSM), digital signal processors (DSP), or combinations thereof. In this embodiment, the processor includes a field programmable logic gate array (FPGA), wherein the arithmetic circuit of the distance measuring device may be a part of the field programmable logic gate array (FPGA). In one example, the method of point cloud noise filtering in the previous embodiment can be implemented in the underlying firmware such as a field programmable logic gate array FPGA in the ranging device, which can utilize the original sampling data (such as the ranging device collected in this article). The feature information of the point cloud points) more accurately distinguish between noise and non-noise. In actual use, the probability of misjudgment and miss-judgment is far less than the existing upper-level algorithm noise filtering scheme based on spatial coordinate relationship.

The distance measuring device includes one or more processors that work together or separately, and the memory is used to store program instructions; the processor is used to execute the program instructions stored in the memory, and when the program instructions are executed, The processor is used for:

Determine the noise in the point cloud data collected in the preset continuous time window according to whether the change of the characteristic information of the continuous point cloud points in the preset continuous time window is continuous, wherein the noise includes At least one point cloud point that is not continuous with the feature information of other point cloud points within the preset continuous time window. Optionally, in the point cloud data, one of the point cloud points is confirmed as a noise point before the other point cloud point is collected. Therefore, the processing result has no delay and high accuracy, and the noise filtering process is almost With the point cloud collection done in real time.

In an example, the point cloud noise filtering method of the embodiment of the present invention implements the noise filtering function in the embedded bottom firmware of the distance measuring device based on the sampling information in the continuous time window, and the embedded bottom firmware includes a data buffer. To store the characteristic information of the point cloud points collected in the continuous time window, the characteristic information includes at least one of the aforementioned depth, scan angle, spatial curvature, pulse waveform characteristics, reflectivity, echo energy, etc. Species or other feature information of point cloud points that can be used for noise filtering. The determining the noise in the point cloud data collected in the preset continuous time window according to whether the change of the characteristic information of the continuous point cloud points in the preset continuous time window is continuous includes: The feature information of the point cloud points collected in the continuous time window is extracted from the data buffer, and the noise points in the point cloud data collected in the preset continuous time window are determined. Therefore, the distance measuring device of the present invention can realize data storage and calculation only by maintaining a small data buffer (such as a data buffer with a size of several KB) in the firmware, with low system overhead and strong platform adaptability. While achieving noise filtering, point cloud data output with almost no delay can be achieved.

In an example, the processor determines the noise points in the point cloud data collected within the preset continuous time window according to whether the characteristic information of the continuous point cloud points within the preset continuous time window changes continuously. The processor is specifically configured to determine the isolation degree of the characteristic information of the current point cloud point, and the isolation degree is used to determine whether the change in the characteristic information of the current point cloud point is the same as that of the current point cloud point. At least one point cloud point in the preceding time window before the point and/or at least one point cloud point in the subsequent time window after the point is continuous; according to the isolation degree of the feature information of the current point cloud point, the determination Whether the current point cloud point is noise.

In an example, when the processor determines whether the current point cloud point is a noise point according to the isolation degree of the feature information of the current point cloud point, the processor is specifically configured to: determine the current point based on a preset mapping function The isolation degree of the feature information of the cloud point; according to the comparison result of the isolation degree of the feature information and a set threshold, it is determined whether the current point cloud point is a noise point, wherein, when the isolation degree of the feature information is greater than all When the threshold is set, it is determined that the current point cloud point is a noise point.

In an example, the feature information includes depth, the preset mapping function is determined based on the difference between the current point cloud point and the depth value of at least one adjacent point cloud point, and the adjacent point cloud point is at the current point cloud point. Describe the point cloud points collected before the current point cloud point. Optionally, the set threshold is determined based on the scanning angular velocity of the distance measuring device, the maximum filtering angle, and the depth value of the current point cloud point, wherein the maximum filtering angle is the distance measurement The angle between the forward direction of the light pulse sequence emitted by the device and the reference plane.

In an example, the feature information includes a scan angle, where the scan angle of the current point cloud point is the space vector of the current point cloud point pointing to an adjacent point cloud point adjacent to the current point cloud point and the current point cloud point The angle between the space vectors of, where the adjacent point cloud point is a point cloud point collected before the current point cloud point.

In an example, the processor determines the noise points in the point cloud data collected within the preset continuous time window according to whether the characteristic information of the continuous point cloud points within the preset continuous time window changes continuously. When the time, the processor is specifically configured to: obtain the scan angle of each point cloud point in a preset continuous time window, wherein the point cloud collected before the first time point in the preset continuous time window The scan angle of the point is above a first threshold scan angle, and the scan angle of at least one point cloud point collected after the first time point is less than the first threshold scan angle and less than the set threshold of the scan angle The point cloud point is noise.

In an example, the processor determines the noise points in the point cloud data collected within the preset continuous time window according to whether the characteristic information of the continuous point cloud points within the preset continuous time window changes continuously. When, the processor is specifically used for:

The scan angle of the point cloud point collected in the previous time window before the current point cloud point, the scan angle of the point cloud point collected in the subsequent time window after the current point cloud point, and the scan angle of the current point cloud point, whichever is the smallest Scan angle value;

According to the comparison result of the minimum scanning angle value and the set threshold value of the scanning angle, it is determined whether the point cloud point corresponding to the minimum scanning angle value is a noise point, wherein, when the minimum scanning angle value is less than the scanning angle When setting the threshold value of the angle, it is determined that the point cloud point corresponding to the minimum scanning angle value is a noise point.

Optionally, when the preceding time window is 0 and the subsequent time window is 1, the minimum scan angle value is the scan angle of the current point cloud point and the phase adjacent to the current point cloud point. The minimum scan angle of the adjacent point cloud point; or, the pre-order time window is 1, and the subsequent time window is 0, and the minimum scan angle value is the sum of the scan angle of the current point cloud point and the current The minimum value of the scan angle of the adjacent point cloud point before the point cloud point.

In an example, the processor determines the noise points in the point cloud data collected within the preset continuous time window according to whether the characteristic information of the continuous point cloud points within the preset continuous time window changes continuously. When the time, the processor is specifically configured to: obtain the scan angle of each point cloud point in a preset continuous time window containing the acquisition time of the current point cloud point; and determine the scan angle of each point cloud point within the preset continuous time window The maximum number of point cloud points for which the collected scan angle is less than the set threshold of the scan angle; according to the comparison result of the maximum number and the preset number, it is determined whether the current point cloud point is a noise point, wherein, when the maximum number When it is less than the preset number, it is determined that the current point cloud point is a noise point.

In an example, the feature information includes spatial curvature, and when the processor determines whether the current point cloud point is a noise according to the comparison result of the isolation degree and a set threshold, it is specifically used to: The result of comparing the isolation degree of the spatial curvature with a set threshold value determines whether the current point cloud point is a noise point, wherein when the isolation degree of the spatial curvature of the current point cloud point is greater than the set threshold value, it is determined The current point cloud point is a noise point.

In one example, the characteristics of the pulse waveform include at least one of pulse width, pulse height, and pulse area of the pulse waveform. The characteristics of the pulse waveform include the pulse width of the pulse waveform, the pulse width being the difference between the cut-off time and the arrival time, wherein the arrival time is the time when the echo signal first triggers the lowest threshold of the time digitizer, The cut-off time is the time when the echo signal triggers the lowest threshold of the time-to-digital converter for the second time.

In an example, the processor determines the noise points in the point cloud data collected within the preset continuous time window according to whether the characteristic information of the continuous point cloud points within the preset continuous time window changes continuously. When, the processor is specifically configured to: determine the abnormal spread of the point cloud points collected in the preset continuous time window according to a preset mapping function; determine the abnormal spread in the preset continuous time window according to the abnormal spread The noise points in the point cloud data collected within a time window, wherein the noise points include point cloud points whose abnormal spread width is greater than a set threshold.

Wherein, the preset mapping function of the current point cloud point is set according to whether the pulse width, arrival time, and cut-off time of the continuous point cloud points of the preset continuous time window meet the preset filter conditions, wherein, when all points are satisfied When the preset filter condition is described, the abnormal spread of the current point cloud point determined according to the preset mapping function is greater than the set threshold. When the preset filter condition is not met, the current point cloud point determined according to the preset mapping function The abnormal spread width of the point cloud point is less than the set threshold.

The pulse width of the echo signal at the first time point in the preorder time window of the current point cloud point has a sudden widening compared with the pulse width at the previous time of the first time point, wherein the sudden widening is greater than The first pulse width setting threshold;

The pulse width of the echo signal at the second time point in the subsequent time window of the current point cloud point suddenly decreases compared to the pulse width at the previous time of the second time point, wherein the sudden decrease is greater than The first pulse width setting threshold;

The arrival time in the time window between the first time point and the second time point is separated from the pre-arrival time of the first time point within a first threshold and the cut-off time in the time window is the same as the first The subsequent cut-off time of the two time points is within a second threshold value, or, the cut-off time in the time window between the first time point and the second time point and the previous cut-off time of the first time point The distance is within the third threshold time and the arrival time in the time window is within the fourth threshold time from the subsequent arrival time of the second time point;

In an example, the characteristic information includes reflectivity, and the processor is further configured to: reduce the reflectivity of continuous point cloud points in any time window within the preset continuous time window from the first reflectivity to Second reflectivity, and according to the time sequence when the reflectivity of the point cloud point increases from the second reflectivity to the third reflectivity, wherein the points in the continuous point cloud point whose reflectivity is equal to or less than the second reflectivity Cloud points are noise points.

In an example, the characteristic information includes reflectivity, and the processor determines whether the characteristic information of the continuous point cloud points in the preset continuous time window changes continuously, and determines the data collected in the preset continuous time window. In the case of noise points in the point cloud data, the processor is specifically configured to: according to a preset mapping function, determine the degree of abrupt change in reflectivity of the point cloud points collected within the preset continuous time window; The degree of sudden change in rate determines the noise points in the point cloud data collected within the preset continuous time window, where the noise points include point cloud points with the degree of sudden change in reflectance greater than a set threshold.

Optionally, the preset mapping function of the current point cloud point is set according to whether the reflectivity of the continuous point cloud points in the preset continuous time window meets a preset filter condition, wherein, when the preset filter condition is satisfied , The reflectivity mutation degree of the current point cloud point determined according to the preset mapping function is greater than a set threshold, and when the preset filter condition is not satisfied, the reflection of the current point cloud point determined according to the preset mapping function The rate of sudden change is less than the set threshold.

Exemplarily, the preset continuous time window includes a pre-order time window and a post-order time window, wherein the pre-order time window is located before the acquisition time of the current point cloud point, and the subsequent time window is located in the current After the point cloud point collection time, the preset filter conditions include the following conditions:

The reflectivity of the point cloud points collected at the first time point of the preamble time window is reduced to below the first threshold reflectivity, and the reflectivity of the point cloud points collected before the first time point is the same as the The difference in reflectivity of the point cloud points collected at the first time point is greater than the set threshold;

The reflectivity of the point cloud points collected at the second time point of the subsequent time window is below the first threshold reflectivity, and the reflectivity of the point cloud points collected after the second time point increases, And the difference between the reflectivity of the point cloud points collected after the second time point and the reflectivity of the point cloud points collected at the second time point is greater than a set threshold;

The reflectivity of all point cloud points collected from the first time point to the second time point is less than the first threshold reflectivity, and the reflectivity of all point cloud points collected from the first time point to the second time point The number of all point cloud points is less than the threshold number; the current point cloud point collection time is between the first time point and the second time point.

In an example, the ranging device further includes: a hardware circuit (not shown) for sampling to obtain the echo energy.

Hereinafter, referring to FIG. 9, the structure of a distance measuring system in the embodiment of the present invention will be described in more detail. The distance measuring system 900 includes a distance measuring device 901 and a display unit 902, wherein the distance measuring device One of the 901 and the display unit 902 is used to implement the relevant steps of the point cloud noise filtering method described above.

In an example, the distance measuring device 901 includes a memory and a processor, configured to store executable instructions, and the processor is configured to execute the instructions stored in the memory, so that the processor performs point cloud filtering. The relevant steps of the method.

In this embodiment, the specific structure and description of the distance measuring device 901 can refer to the structures in FIG. 7 and FIG. 8, which will not be repeated here.

In an example, the display unit 902 is configured to: acquire the point cloud data output by the distance measuring device, wherein the point cloud points in the point cloud data that are determined to be noise points are marked by a flag; and display according to the instruction input by the user The point cloud data after the noise is filtered out or the point cloud data containing the noise is displayed.

The display unit may include a display, an input device, a memory, a processor, and so on. The memory is used for storing processor-executable instructions, for example, for the existence of corresponding steps and program instructions in the method for implementing the point cloud noise filtering of the distance measuring device according to the embodiment of the present invention. It may include one or more computer program products, and the computer program products may include various forms of computer-readable storage media, such as volatile memory and/or non-volatile memory. The volatile memory may include random access memory (RAM) and/or cache memory (cache), for example. The non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, and the like.

The processor of the display unit 902 may be a central processing unit (CPU), an image processing unit (GPU), an application specific integrated circuit (ASIC), a field programmable logic gate array (FPGA), or a data processing capability and/or instruction execution capability Other forms of processing units, and can control other components in the ranging device to perform desired functions. The processor of the display unit 902 can execute the instructions stored in the memory to execute the method for filtering noise from a point cloud of a distance measuring device described herein. For the method for filtering noise, refer to the description in the foregoing embodiment, and will not be omitted here. Repeat the details. For example, the processor can include one or more embedded processors, processor cores, microprocessors, logic circuits, hardware finite state machines (FSM), digital signal processors (DSP), or combinations thereof. In this embodiment, the processor includes a field programmable logic gate array (FPGA).

The input device may be a device used by the user to input instructions (for example, for inputting an instruction for the user to display the point cloud data after filtering out the noise or to display the point cloud data containing the noise), and may include a keyboard, a mouse, and a microphone And one or more of the touch screen, etc.

The display of the display unit may be used to display the point cloud data after filtering the noise or display the point cloud data containing the noise.

The display unit 902 also includes a communication interface (not shown) for communicating with the distance measuring device 901 to obtain the point cloud data output by the distance measuring device, which includes wired or wireless communication. The display unit can be connected to a wireless network based on a communication standard, such as WiFi, 2G, 3G, 4G, 5G, or a combination thereof. In an exemplary embodiment, the communication interface receives a broadcast signal or broadcast related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication interface further includes a near field communication (NFC) module to facilitate short-range communication. For example, the NFC module can be implemented based on radio frequency identification (RFID) technology, infrared data association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology and other technologies.

In an example, the display unit further includes a controller for controlling whether the distance measuring device executes the method according to an instruction input by the user, and when the user needs to execute the point cloud noise filtering method, the first instruction is input , And when the user does not need to perform the point cloud noise filtering method, the second instruction is input, and the ranging device executes the corresponding action according to the received instruction, so as to control whether the ranging device performs the point cloud noise filtering method according to user needs , Increase the flexibility of the ranging system.

In an example, the display unit 902 is further configured to: obtain the point cloud data output by the distance measuring device, the point cloud data may be the original sampling data collected by the distance measuring device, which may include the various features described in the foregoing Information related data; determine whether to perform the point cloud filtering method described above according to the instructions input by the user to filter out the noise in the point cloud data, that is, determine whether the display unit executes the points described above according to the instructions input by the user A method for cloud noise filtering, and real-time display of the point cloud data after filtering the noise or displaying the point cloud data containing the noise. By setting the display unit, the user can view the point cloud data more intuitively, and the display unit can also obtain the original sampling data of the distance measuring device and determine whether to perform the point cloud noise filtering method according to the user’s needs, so as to ensure that the The user performs the point cloud noise filtering method to filter the point cloud data when needed, and can only display the point cloud data when the user does not need to filter the noise, so the flexibility is higher and the user experience is better.

Since the distance measurement system of the embodiment of the present invention can also be used to implement the point cloud noise filtering method described above, it also has the advantages of the point cloud noise filtering method described above.

In summary, the point cloud noise filtering method, ranging device, and ranging system of the embodiments of the present invention have the following advantages: 1) Break through the limitation of single pulse sampling and make full use of the inherent timing information and original sampling data of the detection point, It can effectively filter light noise, rain and fog noise, crosstalk noise, and wire drawing noise that cannot be identified by existing solutions. 2) The point cloud noise filtering method of the present invention can be implemented in the underlying firmware of the ranging device, and can use the original sampling data to more accurately distinguish between noise and non-noise. In actual use, the probability of misjudgment and miss-judgment is much less than The existing upper-level algorithm noise filtering scheme based on the spatial coordinate relationship. (3) The point cloud noise filtering method of the present invention implements the noise filtering function in the embedded bottom firmware of the ranging device based on the sampling information in the continuous time window. The embedded bottom firmware includes a data buffer for realizing data Therefore, the point cloud noise filtering method of the present invention only needs to maintain a data buffer with a size of several KB in the firmware to realize data storage and calculation. The system overhead is small, and the platform is highly adaptable. While filtering noise, point cloud data output with almost no delay can be achieved.

In one embodiment, the distance measuring device of the embodiment of the present invention can be applied to a mobile platform, and the distance measuring device and/or the aforementioned distance measuring system can be installed on the platform body of the mobile platform. A mobile platform with a distance measuring device can measure the external environment, for example, measuring the distance between the mobile platform and obstacles for obstacle avoidance and other purposes, and for two-dimensional or three-dimensional surveying and mapping of the external environment. In some embodiments, the mobile platform includes at least one of an unmanned aerial vehicle, a car, a remote control car, a robot, a boat, and a camera. When the ranging device is applied to an unmanned aerial vehicle, the platform body is the fuselage of the unmanned aerial vehicle. When the distance measuring device is applied to a car, the platform body is the body of the car. The car can be a self-driving car or a semi-automatic driving car, and there is no restriction here. When the distance measuring device is applied to a remote control car, the platform body is the body of the remote control car. When the distance measuring device is applied to a robot, the platform body is a robot. When the distance measuring device is applied to a camera, the platform body is the camera itself.

Since the distance measuring device in the embodiment of the present invention is used to execute the aforementioned method, and the mobile platform includes the distance measuring device, both the distance measuring device and the mobile platform have the same advantages as the aforementioned method.

In addition, the embodiment of the present invention also provides a computer storage medium on which a computer program is stored. One or more computer program instructions can be stored on the computer-readable storage medium, and the processor can run the program instructions stored in the storage device to implement the steps (implemented by the processor) in the embodiments of the present invention described herein. Functions and/or other desired functions, for example, to perform the corresponding steps of the point cloud noise filtering method of the distance measuring device according to the embodiment of the present invention, and various application programs and various applications may be stored in the computer-readable storage medium. Such data, such as various data used and/or generated by the application program.

For example, the computer storage medium may include, for example, a memory card of a smart phone, a storage component of a tablet computer, a hard disk of a personal computer, a read-only memory (ROM), an erasable programmable read-only memory (EPROM), and a portable compact disk. Read only memory (CD-ROM), USB memory, or any combination of the above storage media. The computer-readable storage medium may be any combination of one or more computer-readable storage media. For example, a computer-readable storage medium contains computer-readable program code for converting point cloud data into a two-dimensional image, and/or computer-readable program code for three-dimensional reconstruction of point cloud data, and the like.

It should be understood that each part of this application can be implemented by hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if it is implemented by hardware as in another embodiment, it can be implemented by any one or a combination of the following technologies known in the art: Discrete logic gate circuits with logic functions for data signals Logic circuits, dedicated integrated circuits with suitable combinational logic gate circuits, Programmable Gate Array (Programmable Gate Array; hereinafter referred to as PGA), Field Programmable Gate Array (Field Programmable Gate Array; referred to as FPGA), etc.

Although the exemplary embodiments have been described herein with reference to the accompanying drawings, it should be understood that the above-described exemplary embodiments are merely exemplary, and are not intended to limit the scope of the present invention thereto. Those of ordinary skill in the art can make various changes and modifications therein without departing from the scope and spirit of the present invention. All these changes and modifications are intended to be included within the scope of the present invention as claimed in the appended claims.

A person of ordinary skill in the art may realize that the units and algorithm steps of the examples described in combination with the embodiments disclosed herein can be implemented by electronic hardware or a combination of computer software and electronic hardware. Whether these functions are performed by hardware or software depends on the specific application and design constraint conditions of the technical solution. Professionals and technicians can use different methods for each specific application to implement the described functions, but such implementation should not be considered as going beyond the scope of the present invention.

In the several embodiments provided in this application, it should be understood that the disclosed device and method may be implemented in other ways. For example, the device embodiments described above are only illustrative. For example, the division of the units is only a logical function division, and there may be other divisions in actual implementation, for example, multiple units or components may be combined or It can be integrated into another device, or some features can be ignored or not implemented.

In the instructions provided here, a lot of specific details are explained. However, it can be understood that the embodiments of the present invention can be practiced without these specific details. In some instances, well-known methods, structures, and technologies are not shown in detail, so as not to obscure the understanding of this specification.

Similarly, it should be understood that in order to simplify the present invention and help understand one or more of the various aspects of the invention, in the description of the exemplary embodiments of the present invention, the various features of the present invention are sometimes grouped together into a single embodiment. , Or in its description. However, the method of the present invention should not be construed as reflecting the intention that the claimed invention requires more features than those explicitly stated in each claim. To be more precise, as reflected in the corresponding claims, the point of the invention is that the corresponding technical problems can be solved with features that are less than all the features of a single disclosed embodiment. Therefore, the claims following the specific embodiment are thus explicitly incorporated into the specific embodiment, wherein each claim itself serves as a separate embodiment of the present invention.

Those skilled in the art can understand that in addition to mutual exclusion between the features, any combination of all features disclosed in this specification (including the accompanying claims, abstract, and drawings) and any method or device disclosed in this manner can be used. Processes or units are combined. Unless expressly stated otherwise, each feature disclosed in this specification (including the accompanying claims, abstract and drawings) may be replaced by an alternative feature providing the same, equivalent or similar purpose.

In addition, those skilled in the art can understand that although some embodiments described herein include certain features included in other embodiments but not other features, the combination of features of different embodiments means that they are within the scope of the present invention. Within and form different embodiments. For example, in the claims, any one of the claimed embodiments can be used in any combination.

The various component embodiments of the present invention may be implemented by hardware, or by software modules running on one or more processors, or by a combination of them. Those skilled in the art should understand that a microprocessor or a digital signal processor (DSP) may be used in practice to implement some or all of the functions of some modules according to the embodiments of the present invention. The present invention can also be implemented as a device program (for example, a computer program and a computer program product) for executing part or all of the methods described herein. Such a program for realizing the present invention may be stored on a computer-readable medium, or may have the form of one or more signals. Such a signal can be downloaded from an Internet website, or provided on a carrier signal, or provided in any other form.

It should be noted that the above-mentioned embodiments illustrate rather than limit the present invention, and those skilled in the art can design alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses should not be constructed as a limitation to the claims. The invention can be implemented by means of hardware comprising several different elements and by means of a suitably programmed computer. In the unit claims that list several devices, several of these devices may be embodied in the same hardware item. The use of the words first, second, and third, etc. do not indicate any order. These words can be interpreted as names.

Claims

A method for filtering noise from a point cloud of a distance measuring device, characterized in that the method comprises:

Obtain feature information of each point cloud point in the point cloud data collected by the distance measuring device within a preset continuous time window, where the feature information includes at least one of the following: depth, scan angle, spatial curvature, pulse waveform Characteristics, reflectivity, echo energy;

Determine the noise in the point cloud data collected in the preset continuous time window according to whether the change of the characteristic information of the continuous point cloud points in the preset continuous time window is continuous, wherein the noise includes At least one point cloud point that is not continuous with the feature information of other point cloud points within the preset continuous time window.
The method according to claim 1, characterized in that, according to whether the change of the characteristic information of the continuous point cloud points in the preset continuous time window is continuous, the data collected in the preset continuous time window are determined The noise in the point cloud data includes:

Determine the isolation degree of the feature information of the current point cloud point, where the isolation degree is used to determine whether the change in the feature information of the current point cloud point is at least one in the previous time window before the current point cloud point The point cloud point and/or at least one point cloud point in the subsequent subsequent time window are continuous;

According to the isolation degree of the characteristic information of the current point cloud point, it is determined whether the current point cloud point is a noise point.
The method according to claim 2, wherein the determining whether the current point cloud point is a noise point according to the isolation degree of the characteristic information of the current point cloud point comprises:

Determining the isolation degree of the feature information of the current point cloud point based on a preset mapping function;

Determine whether the current point cloud point is a noise point according to the comparison result of the isolation degree of the feature information and a set threshold, wherein, when the isolation degree of the feature information is greater than the set threshold, determine the current point Cloud points are noise points.
The method according to claim 3, wherein the characteristic information includes depth, and the preset mapping function is determined based on the difference between the current point cloud point and the depth value of at least one adjacent point cloud point, and The adjacent point cloud point is a point cloud point collected before the current point cloud point.
The method according to claim 4, wherein the set threshold value is determined based on the scanning angular velocity of the distance measuring device, the maximum filtering angle and the depth value of the current point cloud point, wherein the The maximum filtering angle is the angle between the forward direction of the light pulse sequence emitted by the distance measuring device and the reference plane.
The method according to claim 1, wherein the characteristic information includes a scan angle, wherein the scan angle of the current point cloud point is that the current point cloud point points to an adjacent point cloud point adjacent to the current point cloud point The angle between the space vector of and the space vector of the current point cloud point, where the adjacent point cloud point is a point cloud point collected before the current point cloud point.
The method according to claim 6, characterized in that, according to whether the change of the characteristic information of the continuous point cloud points in the preset continuous time window is continuous, the data collected in the preset continuous time window are determined The noise in the point cloud data includes:

Acquire the scan angle of each point cloud point in the preset continuous time window, where the scan angle of the point cloud point collected before the first time point in the preset continuous time window is located in the first threshold scan If the scan angle of at least one point cloud point collected after the first time point is less than the first threshold scan angle and less than the set threshold of the scan angle, point cloud points with a scan angle less than the set threshold of the scan angle are noise points.
The method according to claim 6, characterized in that, according to whether the change of the characteristic information of the continuous point cloud points in the preset continuous time window is continuous, the data collected in the preset continuous time window are determined The noise in the point cloud data includes:

The scan angle of the point cloud point collected in the previous time window before the current point cloud point, the scan angle of the point cloud point collected in the subsequent time window after the current point cloud point, and the scan angle of the current point cloud point, whichever is the smallest Scan angle value;

According to the comparison result of the minimum scanning angle value and the set threshold value of the scanning angle, it is determined whether the point cloud point corresponding to the minimum scanning angle value is a noise point, wherein, when the minimum scanning angle value is less than the scanning angle When setting the threshold value of the angle, it is determined that the point cloud point corresponding to the minimum scanning angle value is a noise point.
The method according to claim 8, wherein when the preceding time window is 0 and the subsequent time window is 1, the minimum scan angle value is the sum of the scan angle of the current point cloud point and the The minimum value among the scan angles of adjacent point cloud points after the current point cloud point;

Alternatively, the pre-order time window is 1, and the subsequent time window is 0, and the minimum scanning angle value is the scanning angle of the current point cloud point and the adjacent point cloud point before and adjacent to the current point cloud point The minimum value of the scan angle.
The method according to claim 6, characterized in that, according to whether the change of the characteristic information of the continuous point cloud points in the preset continuous time window is continuous, the data collected in the preset continuous time window are determined The noise in the point cloud data includes:

Acquiring the scan angle of each point cloud point in the preset continuous time window including the acquisition time of the current point cloud point;

Determining the maximum number of point cloud points whose scan angle is less than the set threshold of the scan angle collected within the preset continuous time window;

According to a comparison result of the maximum number and a preset number, determine whether the current point cloud point is a noise point, wherein when the maximum number is less than the preset number, it is determined that the current point cloud point is a noise point.
The method according to claim 3, wherein the characteristic information includes a spatial curvature, and determining whether the current point cloud point is a noise according to a comparison result of the isolation degree and a set threshold value comprises:

According to the comparison result of the isolation degree of the spatial curvature of the current point cloud point with a set threshold, it is determined whether the current point cloud point is a noise point, wherein, when the isolation degree of the spatial curvature of the current point cloud point is greater than When setting the threshold, it is determined that the current point cloud point is a noise point.
The method according to claim 1, wherein the characteristics of the pulse waveform include at least one of a pulse width, a pulse height, and a pulse area of the pulse waveform.
The method of claim 1, wherein the characteristics of the pulse waveform include the pulse width of the pulse waveform, and the pulse width is the difference between the cut-off time and the arrival time, wherein the arrival time is the echo signal The time when the lowest threshold of the time digitizer is triggered for the first time, and the cut-off time is the time when the echo signal triggers the lowest threshold of the time digitizer for the second time.
The method according to claim 13, characterized in that, according to whether the change of the characteristic information of the continuous point cloud points in the preset continuous time window is continuous, the data collected in the preset continuous time window are determined The noise in the point cloud data includes:

According to a preset mapping function, determine the abnormal spreading width of the point cloud points collected within the preset continuous time window;

The noise points in the point cloud data collected within the preset continuous time window are determined according to the abnormal spread width, where the noise points include point cloud points with the abnormal spread width greater than a set threshold.
The method of claim 14, wherein the preset mapping function of the current point cloud point is based on whether the pulse width, arrival time, and cut-off time of the continuous point cloud points of the preset continuous time window meet the preset The filter condition is set, wherein, when the preset filter condition is met, the abnormal spread of the current point cloud point determined according to the preset mapping function is greater than a set threshold, and when the preset filter condition is not met , The abnormal spread width of the current point cloud point determined according to the preset mapping function is less than a set threshold.
The method of claim 15, wherein when the pulse width, arrival time, and cut-off time meet the preset filter conditions, it is determined that the current point cloud point is a noise point, and the preset filter conditions include the following conditions At least one of:

The pulse width of the echo signal at the first time point in the preorder time window of the current point cloud point has a sudden widening compared with the pulse width at the previous time of the first time point, wherein the sudden widening is greater than The first pulse width setting threshold;

The pulse width of the echo signal at the second time point in the subsequent time window of the current point cloud point suddenly decreases compared to the pulse width at the previous time of the second time point, wherein the sudden decrease is greater than The first pulse width setting threshold;

The arrival time in the time window between the first time point and the second time point is separated from the pre-arrival time of the first time point within a first threshold and the cut-off time in the time window is the same as the first The subsequent cut-off time of the two time points is within a second threshold value, or, the cut-off time in the time window between the first time point and the second time point and the previous cut-off time of the first time point The distance is within the third threshold time and the arrival time in the time window is within the fourth threshold time from the subsequent arrival time of the second time point;

The current point cloud point is within a time window between the first time point and the second time point.
The method according to claim 1, wherein the characteristic information includes reflectivity, and it is determined whether the characteristic information of consecutive point cloud points within a preset continuous time window changes continuously. The noise in the point cloud data collected in a continuous time window includes:

The reflectivity of the continuous point cloud points in any time window within the preset continuous time window decreases from the first reflectivity to the second reflectivity, and according to the time sequence when the reflectivity of the point cloud points increases from the second reflectivity Up to the third reflectivity, wherein, among the continuous point cloud points, the point cloud points with the reflectivity equal to or less than the second reflectivity are noise points.
The method according to claim 17, wherein the characteristic information includes reflectivity, and it is determined whether the change in the characteristic information of consecutive point cloud points within a preset continuous time window is continuous. The noise in the point cloud data collected in a continuous time window includes:

Determine, according to a preset mapping function, the degree of abrupt change in reflectivity of the point cloud points collected within the preset continuous time window;

The noise points in the point cloud data collected within the preset continuous time window are determined according to the reflectivity abrupt change degree, wherein the noise points include point cloud points with the reflectance abrupt change degree greater than a set threshold.
The method according to claim 18, wherein the preset mapping function of the current point cloud point is set according to whether the reflectivity of the continuous point cloud points of the preset continuous time window meets the preset filter condition, wherein, When the preset filter condition is met, the sudden change in reflectance of the current point cloud point determined according to the preset mapping function is greater than a set threshold, and when the preset filter condition is not met, according to the preset mapping The sudden change degree of reflectivity of the current point cloud point determined by the function is less than the set threshold.
The method according to claim 19, wherein the preset continuous time window comprises a pre-order time window and a post-order time window, wherein the pre-order time window is located before the collection time of the current point cloud point, so The latter time window is located after the collection time of the current point cloud point, and the preset filtering conditions include the following conditions:

The reflectivity of the point cloud points collected at the first time point of the preamble time window is reduced to below the first threshold reflectivity, and the reflectivity of the point cloud points collected before the first time point is the same as the The difference in reflectivity of the point cloud points collected at the first time point is greater than the set threshold;

The reflectivity of the point cloud points collected at the second time point of the subsequent time window is below the first threshold reflectivity, and the reflectivity of the point cloud points collected after the second time point increases, And the difference between the reflectivity of the point cloud points collected after the second time point and the reflectivity of the point cloud points collected at the second time point is greater than a set threshold;

The reflectivity of all point cloud points collected from the first time point to the second time point is less than the first threshold reflectivity, and the reflectivity of all point cloud points collected from the first time point to the second time point The number of all point cloud points is less than the threshold number;

The collection time of the current point cloud point is between the first time point and the second time point.
The method according to any one of claims 1 to 20, wherein in the point cloud data, one of the point cloud points is confirmed as a noise point before the other point cloud point is collected.
The method according to any one of claims 1 to 21, wherein the method is implemented in a field programmable logic gate array (FPGA) in the distance measuring device.
The method according to any one of claims 1 to 22, wherein the embedded underlying firmware in the distance measuring device includes a data buffer for storing the characteristics of the point cloud points collected in a continuous time window information;

The determining the noise points in the point cloud data collected within the preset continuous time window according to whether the change of the characteristic information of the continuous point cloud points within the preset continuous time window is continuous includes:

The feature information of the point cloud points collected in the continuous time window is retrieved from the data buffer, and the noise points in the point cloud data collected in the preset continuous time window are determined.
The method according to any one of claims 1 to 22, wherein the method is implemented in a display unit, wherein the display unit is communicatively connected with the distance measuring device.
A distance measuring device, characterized in that the distance measuring device includes one or more processors, which work together or separately, and the processors are used for:

Obtain feature information of each point cloud point in the point cloud data collected by the distance measuring device within a preset continuous time window, where the feature information includes at least one of the following: depth, scan angle, spatial curvature, pulse waveform Characteristics, reflectivity, echo energy;

Determine the noise in the point cloud data collected in the preset continuous time window according to whether the change of the characteristic information of the continuous point cloud points in the preset continuous time window is continuous, wherein the noise includes At least one point cloud point that is not continuous with the feature information of other point cloud points within the preset continuous time window.
The distance measuring device according to claim 25, wherein the processor is further configured to:

Determine the isolation degree of the feature information of the current point cloud point, where the isolation degree is used to determine whether the change in the feature information of the current point cloud point is at least one in the previous time window before the current point cloud point The point cloud point and/or at least one point cloud point in the subsequent subsequent time window are continuous;

According to the isolation degree of the characteristic information of the current point cloud point, it is determined whether the current point cloud point is a noise point.
The distance measuring device according to claim 26, wherein the processor is further configured to:

Determining the isolation degree of the feature information of the current point cloud point based on a preset mapping function;

Determine whether the current point cloud point is a noise point according to the comparison result of the isolation degree of the feature information and a set threshold, wherein, when the isolation degree of the feature information is greater than the set threshold, determine the current point Cloud points are noise.
The distance measuring device according to claim 27, wherein the characteristic information includes depth, and the preset mapping function is determined based on the difference between the depth value of the current point cloud point and at least one adjacent point cloud point, The adjacent point cloud point is a point cloud point collected before the current point cloud point.
The distance measuring device according to claim 27, wherein the set threshold value is determined based on the scanning angular velocity of the distance measuring device, the maximum filtering angle and the depth value of the current point cloud point, wherein, The maximum filtering angle is the angle between the advancing direction of the light pulse sequence emitted by the distance measuring device and the reference plane.
The distance measuring device according to claim 25, wherein the characteristic information includes a scan angle, wherein the scan angle of the current point cloud point is that the current point cloud point points to an adjacent point adjacent to the current point cloud point The angle between the space vector of the cloud point and the space vector of the current point cloud point, wherein the adjacent point cloud point is a point cloud point collected before the current point cloud point.
The distance measuring device according to claim 30, wherein the processor is further configured to:

Acquire the scan angle of each point cloud point in the preset continuous time window, where the scan angle of the point cloud point collected before the first time point in the preset continuous time window is located in the first threshold scan If the scan angle of at least one point cloud point collected after the first time point is less than the first threshold scan angle and less than the set threshold of the scan angle, point cloud points with a scan angle less than the set threshold of the scan angle are noise points.
The distance measuring device according to claim 30, wherein the processor is further configured to:

The scan angle of the point cloud point collected in the previous time window before the current point cloud point, the scan angle of the point cloud point collected in the subsequent time window after the current point cloud point, and the scan angle of the current point cloud point, whichever is the smallest Scan angle value;

According to the comparison result of the minimum scanning angle value and the set threshold value of the scanning angle, it is determined whether the point cloud point corresponding to the minimum scanning angle value is a noise point, wherein, when the minimum scanning angle value is less than the scanning angle When setting the threshold value of the angle, it is determined that the point cloud point corresponding to the minimum scanning angle value is a noise point.
The distance measuring device according to claim 32, wherein when the preceding time window is 0 and the subsequent time window is 1, the minimum scanning angle value is the sum of the scanning angle of the current point cloud point and The minimum value among the scan angles of adjacent point cloud points after the current point cloud point;

Alternatively, the pre-order time window is 1, and the subsequent time window is 0, and the minimum scanning angle value is the scanning angle of the current point cloud point and the adjacent point cloud point before and adjacent to the current point cloud point The minimum value of the scan angle.
The distance measuring device according to claim 30, wherein the processor is further configured to:

Acquiring the scan angle of each point cloud point in the preset continuous time window including the acquisition time of the current point cloud point;

Determining the maximum number of point cloud points whose collected scan angle is less than the set threshold of the scan angle within the preset continuous time window;

According to a comparison result of the maximum number and a preset number, determine whether the current point cloud point is a noise point, wherein when the maximum number is less than the preset number, it is determined that the current point cloud point is a noise point.
The distance measuring device according to claim 27, wherein the characteristic information includes a spatial curvature, and determining whether the current point cloud point is a noise according to a comparison result of the isolation degree and a set threshold value comprises:

According to the comparison result of the isolation degree of the spatial curvature of the current point cloud point with a set threshold, it is determined whether the current point cloud point is a noise point, wherein, when the isolation degree of the spatial curvature of the current point cloud point is greater than When setting the threshold, it is determined that the current point cloud point is a noise point.
The distance measuring device according to claim 25, wherein the characteristics of the pulse waveform include at least one of a pulse width, a pulse height, and a pulse area of the pulse waveform.
The distance measuring device according to claim 25, wherein the characteristics of the pulse waveform include the pulse width of the pulse waveform, and the pulse width is the difference between the cut-off time and the arrival time, wherein the arrival time is the return value. The time when the wave signal triggers the lowest threshold of the time digitizer for the first time, and the cut-off time is the time when the echo signal triggers the lowest threshold of the time digitizer for the second time.
The distance measuring device according to claim 37, wherein the processor is further configured to:

According to a preset mapping function, determine the abnormal spreading width of the point cloud points collected within the preset continuous time window;

The noise points in the point cloud data collected within the preset continuous time window are determined according to the abnormal spread width, where the noise points include point cloud points with the abnormal spread width greater than a set threshold.
The distance measuring device according to claim 38, wherein the preset mapping function of the current point cloud point is based on whether the pulse width, arrival time and cut-off time of the continuous point cloud points of the preset continuous time window satisfy Preset filter conditions are set, wherein, when the preset filter conditions are met, the abnormal spread of the current point cloud point determined according to the preset mapping function is greater than a set threshold, and when the preset filter conditions are not met When the condition is met, the abnormal expansion width of the current point cloud point determined according to the preset mapping function is less than the set threshold.
The distance measuring device according to claim 39, wherein when the pulse width, arrival time, and cut-off time satisfy the preset filter condition, it is determined that the current point cloud point is a noise point, and the preset filter condition comprises At least one of the following conditions:

The pulse width of the echo signal at the first time point in the preorder time window of the current point cloud point has a sudden widening compared with the pulse width at the previous time of the first time point, wherein the sudden widening is greater than The first pulse width setting threshold;

The pulse width of the echo signal at the second time point in the subsequent time window of the current point cloud point suddenly decreases compared to the pulse width at the previous time of the second time point, wherein the sudden decrease is greater than The first pulse width setting threshold;

The arrival time in the time window between the first time point and the second time point is separated from the pre-arrival time of the first time point within a first threshold and the cut-off time in the time window is the same as the first The subsequent cut-off time of the two time points is within a second threshold value, or, the cut-off time in the time window between the first time point and the second time point and the previous cut-off time of the first time point The distance is within the third threshold time and the arrival time in the time window is within the fourth threshold time from the subsequent arrival time of the second time point;

The current point cloud point is within a time window between the first time point and the second time point.
The distance measuring device according to claim 25, wherein the characteristic information includes reflectivity, and the processor is further configured to:

The reflectivity of the continuous point cloud points in any time window within the preset continuous time window decreases from the first reflectivity to the second reflectivity, and according to the time sequence when the reflectivity of the point cloud points increases from the second reflectivity Up to the third reflectivity, wherein, among the continuous point cloud points, the point cloud points with the reflectivity equal to or less than the second reflectivity are noise points.
The distance measuring device according to claim 41, wherein the characteristic information includes reflectivity, and the processor is further configured to:

Determine, according to a preset mapping function, the degree of abrupt change in reflectivity of the point cloud points collected within the preset continuous time window;

The noise points in the point cloud data collected within the preset continuous time window are determined according to the reflectivity abrupt change degree, wherein the noise points include point cloud points with the reflectance abrupt change degree greater than a set threshold.
The distance measuring device according to claim 42, wherein the preset mapping function of the current point cloud point is set according to whether the reflectivity of the continuous point cloud points of the preset continuous time window meets the preset filter condition, Wherein, when the preset filter condition is met, the reflectivity mutation degree of the current point cloud point determined according to the preset mapping function is greater than a set threshold, and when the preset filter condition is not met, according to the preset It is assumed that the reflectivity mutation degree of the current point cloud point determined by the mapping function is less than the set threshold.
The distance measuring device according to claim 43, wherein the preset continuous time window comprises a pre-order time window and a post-order time window, wherein the pre-order time window is located before the collection time of the current point cloud point , The subsequent time window is located after the collection time of the current point cloud point, and the preset filtering conditions include the following conditions:

The reflectivity of the point cloud points collected at the first time point of the preamble time window is reduced to below the first threshold reflectivity, and the reflectivity of the point cloud points collected before the first time point is the same as the The difference in reflectivity of the point cloud points collected at the first time point is greater than the set threshold;

The reflectivity of the point cloud points collected at the second time point of the subsequent time window is below the first threshold reflectivity, and the reflectivity of the point cloud points collected after the second time point increases, And the difference between the reflectivity of the point cloud points collected after the second time point and the reflectivity of the point cloud points collected at the second time point is greater than a set threshold;

The reflectivity of all point cloud points collected from the first time point to the second time point is less than the first threshold reflectivity, and the reflectivity of all point cloud points collected from the first time point to the second time point The number of all point cloud points is less than the threshold number;

The collection time of the current point cloud point is between the first time point and the second time point.
The distance measuring device according to any one of claims 25 to 44, wherein in the point cloud data, one of the point cloud points is confirmed as a noise point before the other point cloud point is collected.
The distance measuring device according to any one of claims 25 to 45, wherein the processor comprises a field programmable logic gate array (FPGA).
The distance measuring device according to any one of claims 25 to 45, wherein the embedded underlying firmware in the distance measuring device includes a data buffer for storing the point cloud points collected in a continuous time window Characteristic information;

The determining the noise points in the point cloud data collected within the preset continuous time window according to whether the change of the characteristic information of the continuous point cloud points within the preset continuous time window is continuous includes:

The feature information of the point cloud points collected in the continuous time window is retrieved from the data buffer, and the noise points in the point cloud data collected in the preset continuous time window are determined.
A distance measuring system, characterized in that the distance measuring system comprises a distance measuring device and a display unit, wherein one of the distance measuring device and the display unit is used to implement any one of claims 1 to 24 The method described.
The distance measurement system according to claim 48, wherein the distance measurement device comprises a memory and a processor, configured to store executable instructions, and the processor is configured to execute the instructions stored in the memory, so that The processor executes the method.
The distance measuring system according to claim 49, wherein the display unit is used for:

Acquiring point cloud data output by the distance measuring device, wherein the point cloud points in the point cloud data that are determined to be noise points are marked by flag bits;

According to an instruction input by the user, the point cloud data after the noise is filtered out or the point cloud data containing the noise is displayed.
The distance measuring system according to claim 48, wherein the display unit is further configured to control whether the distance measuring device executes the method according to an instruction input by a user.
The distance measuring system according to claim 48, wherein the display unit is further used for:

Acquiring point cloud data output by the distance measuring device;

Determine whether to execute the method according to any one of claims 1 to 21 according to an instruction input by the user to filter out the noise in the point cloud data;

as well as

Display the point cloud data after filtering the noise or display the point cloud data containing the noise in real time.
A computer storage medium having a computer program stored thereon, characterized in that the method according to any one of claims 1 to 24 is implemented when the program is executed by a processor.
A mobile platform, characterized in that the mobile platform includes:

The distance measuring device according to any one of claims 25 to 47 or the distance measuring system according to any one of claims 48 to 52; and

The platform body, the distance measuring device is installed on the platform body.
The mobile platform of claim 54, wherein the mobile platform comprises an unmanned aerial vehicle, a robot, a vehicle, or a boat.