CN112912756A

CN112912756A - Point cloud noise filtering method, distance measuring device, system, storage medium and mobile platform

Info

Publication number: CN112912756A
Application number: CN201980031289.4A
Authority: CN
Inventors: 吴特思; 王闯; 陈涵
Original assignee: SZ DJI Technology Co Ltd
Current assignee: SZ DJI Technology Co Ltd
Priority date: 2019-09-17
Filing date: 2019-09-17
Publication date: 2021-06-04
Also published as: WO2021051281A1

Abstract

A method for filtering noise of point cloud, a distance measuring device, a system, a storage medium and a mobile platform are provided, the method comprises the following steps: acquiring characteristic information of each point cloud point in point cloud data acquired by a distance measuring device in a preset continuous time window (S501), wherein the characteristic information comprises at least one of the following: depth, scan angle, spatial curvature, characteristics of pulse waveform, reflectivity, echo energy; determining noise points 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 or not (S502), wherein the noise points comprise at least one point cloud point which is discontinuous with the characteristic information of other point cloud points in the preset continuous time window. The method has the advantages that the inherent time sequence information and original sampling data of the point cloud points in the point cloud data acquired by the distance measuring device are fully utilized, light noise, rain fog noise, crosstalk noise, wire drawing noise and the like can be effectively filtered, and the point cloud quality is obviously improved.

Description

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

Description

Technical Field

The invention relates to the technical field of distance measuring devices, in particular to a point cloud noise filtering method, a distance measuring device, a point cloud noise filtering system, a storage medium and a mobile platform.

Background

In actual use, due to structural, optical and hardware design limitations and the influence of an external complex environment, the sensing and measuring equipment for forming the spatial three-dimensional point cloud through continuous single-point ranging may have a situation that the deviation of a single-point ranging calculation result is large, and finally noise is formed in the three-dimensional point cloud.

Laser radar is a typical perception measurement device for forming space three-dimensional point cloud by continuous single-point ranging, and the problem of point cloud noise is always one of the core problems to be solved urgently in the field. Generally, the laser radar noise is mainly generated by the following two reasons: (1) some light pulse signals (such as sunlight, background light noise, rain, snow and dust, crosstalk of other radars and the like) which are not reflected by a measurement target object are received by the radar, but are mistakenly taken as normal echo signals to be calculated due to the limitation of optical, hardware or algorithm design, so that noise points are generated; (2) the normal pulse echo reflected by the measurement target object is distorted in the analog circuit, so that the calculation deviation of the depth calculation model is caused. Both of these types of noise sources are difficult to directly identify and filter by their own characteristics of the sampled signal.

Therefore, in view of the above problems, the present invention provides a method, a ranging apparatus, a system, a storage medium, and a mobile platform for point cloud noise filtering of a ranging apparatus.

Disclosure of Invention

The present invention has been made to solve at least one of the above problems. Specifically, the invention provides a method for filtering noise of a point cloud of a distance measuring device, which comprises the following steps:

acquiring characteristic information of each point cloud point in point cloud data acquired by the distance measuring device in a preset continuous time window, wherein the characteristic information comprises at least one of the following information: depth, scan angle, spatial curvature, characteristics of pulse waveform, reflectivity, echo energy;

and determining noise points in the point cloud data collected in a 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 or not, wherein the noise points comprise at least one point cloud point which is discontinuous with the characteristic information of other point cloud points in the preset continuous time window.

Yet another aspect of the invention provides a ranging device comprising one or more processors, working collectively or individually, the processors being configured to:

In still another aspect, the present invention provides a distance measuring system, which includes a distance measuring device and a display unit, wherein one of the distance measuring device and the display unit is used for implementing the method for filtering noise of point cloud.

Another aspect of the present invention provides a computer storage medium having a computer program stored thereon, which when executed by a processor, implements the method of point cloud noise filtering of the aforementioned distance measuring device.

Yet another aspect of the present invention provides a mobile platform, comprising:

the aforementioned distance measuring device; and

the platform body, range unit installs on the platform body.

The method, the distance measuring device and the system break through the limitation of single pulse sampling, make full use of the inherent time sequence information and original sampling data of the point cloud points in the point cloud data acquired by the distance measuring device, can effectively filter light noise points, rain fog noise points, crosstalk noise points, wire drawing noise points and the like, obviously reduce abnormal noise points in the point cloud data output by the distance measuring device, and obviously optimize and improve the point cloud quality; in addition, the method of the embodiment of the invention can be realized only by less calculation amount, the processing result has no time delay and high accuracy, and the process of noise filtering is almost completed in real time along with the point cloud acquisition.

Drawings

In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive labor.

FIG. 1 is a schematic diagram illustrating noise caused by direct sunlight entering a lidar in one embodiment of the invention;

FIG. 2 is a schematic diagram illustrating the generation principle of multiple echoes of a laser ranging system in an embodiment of the present invention;

FIG. 3 is a schematic diagram illustrating the principle of generating multi-echo fusion noise in a laser ranging system according to an embodiment of the present invention;

FIG. 4 illustrates a schematic diagram of the wiredrawing noise in a point cloud in one embodiment of the invention;

FIG. 5 is a schematic flow chart diagram illustrating a method for point cloud noise filtering for a range finder apparatus in an embodiment of the invention;

FIG. 6 shows a schematic diagram of a bi-prism lidar scanning track in an embodiment of the invention;

FIG. 7 is a schematic diagram of a ranging apparatus according to an embodiment of the invention;

FIG. 8 shows a schematic view of a distance measuring device in an embodiment of the invention;

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

Detailed Description

In order to make the objects, technical solutions and advantages of the present invention more apparent, exemplary embodiments according to the present invention will be described in detail below with reference to the accompanying drawings. It is to be understood that the described embodiments are merely a subset of embodiments of the invention and not all embodiments of the invention, with the understanding that the invention is not limited to the example embodiments described herein. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the invention described herein without inventive step, shall fall within the scope of protection of the invention.

In the following description, numerous specific details are set forth in order to provide a more thorough understanding of the present invention. It will be apparent, however, to one skilled in the art, that the present invention may be practiced without one or more of these specific details. In other instances, well-known features have not been described in order to avoid obscuring the invention.

It is to be understood that the present invention may be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art.

The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" and/or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. As used herein, the term "and/or" includes any and all combinations of the associated listed items.

In order to provide a thorough understanding of the present invention, a detailed structure will be set forth in the following description in order to explain the present invention. Alternative embodiments of the invention are described in detail below, however, the invention may be practiced in other embodiments that depart from these specific details.

The distance measurement principle of the laser distance measurement system is that laser pulses are actively transmitted to a detected object, laser echo signals are received, the distance of the detected object is calculated according to the time difference between the transmission and the reception of the laser, and the angle information of the detected object is obtained based on the known transmission direction of the laser. By means of high-frequency transmitting and receiving, the distance and angle information of massive detection points is obtained, and point cloud is formed, so that three-dimensional reconstruction of surrounding scenes is achieved.

Generally, the laser radar noise is mainly generated by the following two reasons:

1. some light pulse signals (such as sunlight, background light noise, rain, snow and dust, crosstalk of other radars and the like) which are not reflected by a measurement target object are received by the radar, but are mistakenly taken as normal echo signals to be calculated due to the limitation of optical, hardware or algorithm design, so that noise is generated. The above noise points can be classified into three categories: (a) signals formed by sunlight or diffuse reflection thereof are referred to as light noise points for short, such as noise points in a rectangular frame shown in fig. 1; (b) rain, fog, snow, dust and other tiny particles dispersed in the air, which are referred to as rain and fog noise points for short; (c) and other laser radar signal crosstalk operating in the same wavelength range is referred to as crosstalk noise.

2. The normal pulse echo reflected by the measurement target object is distorted in the analog circuit, so that the calculation deviation of the depth calculation model is caused. One of the most common cases is where the target direction has two or more objects that are in close proximity, as shown in figure 2. Whereas laser light emitted by lidar generally has a certain divergence angle theta, which means that the spot is at a certain angle from the point of emissionThe area will increase with increasing distance. Suppose the emission point is d₁The spot area of the laser is S_d1Due to the complexity of the actual environment, the spot S_d1Either all or only a part of the objects A and the other part of the objects A are located at a distance d₂On another object B (which may fall on several different objects in succession in the case of a complex scene). All the light spots form echoes and are received by the system ranging module, so that the receiving module continuously receives two or more echoes in a very short time, and due to the bandwidth limitation of an analog circuit, the multiple echoes are combined into one echo, as shown in fig. 3. After the calculation of the algorithm model, the point cloud usually has a "wire drawing" phenomenon between adjacent objects, so that the noise is called wire drawing noise, as shown in fig. 4.

Because the light speed is constant in a specific environment, if the distance between d1 and d2 is longer, the receiving system can acquire two mutually independent laser echoes to be respectively processed, and correct calculation is carried out; if d1 is closer to d2, the two echoes are fused but the system can still resolve the two echoes, and the receiving system switches the matched computation model to perform correct computation; however, when the distance between d1 and d2 is less than the limit distance dmin, the fusion of the two laser echoes cannot be resolved by the system, and can be mistakenly calculated as a single echo, as shown in fig. 3, which causes noise.

The existing noise filtering method can be generally divided into a product bottom noise filtering scheme and an upper algorithm noise filtering scheme, and the limitations of the existing scheme are summarized as follows:

1. the noise filtering scheme of the bottom layer of the product is usually judged based on single pulse sampling information, and only specific noise points (such as rain, snow and dust) can be filtered in a limited way, while the noise points mentioned in the foregoing are difficult to be screened from the single pulse sampling information, so the noise filtering effect of the scheme is not ideal.

2. The upper-layer algorithm noise filtering scheme is generally based on the spatial coordinate relationship between each point in the point cloud and its adjacent points, and processes the whole frame of point cloud after accumulating for a long time. Moreover, due to bandwidth limitation, the upper-layer algorithm can only acquire limited information such as xyz coordinates and reflection intensity of the point cloud, and the time sequence relationship between radar scanning points and the original sampling data information of the pulse signal are lost correspondingly. The above factors cause the noise filtering scheme to have the following disadvantages: 1) only sparse scatter in space can be filtered out. However, the two noise points mentioned above often appear as clusters or continuations, and thus the effectiveness of this noise filtering strategy is very limited. 2) The noise filtering calculation through the spatial relationship usually requires that the density of point cloud is extremely high, otherwise, the sparse noise points are difficult to accurately identify, but most practical application scenes do not have enough time to accumulate high-density point cloud, so that the noise filtering effect of the upper-layer algorithm is often poor. 3) The processing needs to be performed after accumulating a certain time, and the amount of three-dimensional computation is large, thus resulting in a high output delay. (4) The computational power requirement is relatively high, a special high-performance operation platform is needed, and the implementation in embedded firmware of the sensor is difficult.

In view of the above problems, an embodiment of the present invention provides a method for filtering noise of a point cloud of a distance measuring device, as shown in fig. 5, the method includes the following steps: in step S501, feature information of each point cloud point in the point cloud data collected by the distance measuring device in a preset continuous time window is obtained, where the feature information includes at least one of the following: depth, scan angle, spatial curvature, characteristics of pulse waveform, reflectivity, echo energy; in step S502, noise points in the point cloud data collected in a preset continuous time window are determined according to whether the change of the feature information of continuous point cloud points in the preset continuous time window is continuous, where the noise points include at least one point cloud point which is discontinuous from the feature information of other point cloud points in the preset continuous time window. By the 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 obtained, and the noise point in the point cloud data collected in the preset continuous time window is determined according to whether the change of the characteristic information of the continuous point cloud point in the preset continuous time window is continuous, so that the noise point in the point cloud data is identified and filtered, and therefore, the method has the following advantages: 1) the method breaks through the limitation of single pulse sampling, makes full use of the inherent time sequence information and original sampling data of the point cloud points in the point cloud data acquired by the distance measuring device, and can effectively filter light noise, rain fog noise, crosstalk noise, wire drawing noise and the like which cannot be identified by the existing scheme. 2) The embodiment of the invention is realized in the bottom firmware of the product, can more accurately distinguish noise points and non-noise points by utilizing original sampling data, and has the probability of misjudgment and missed judgment which is far less than that of the existing upper-layer algorithm noise filtering scheme based on the space coordinate relation in actual use. 3) The invention realizes the noise filtering function in the embedded bottom firmware based on the sampling information in the continuous time window, thus the data storage and calculation can be realized only by maintaining a data buffer area with the size of several KB in the firmware, the system overhead is low, the platform adaptability is strong, and the noise filtering can be realized while the point cloud data output with almost no delay can be realized.

The method for filtering noise of point cloud of the ranging apparatus of the present application is described in detail below with reference to the accompanying drawings. The features of the following examples and embodiments may be combined with each other without conflict.

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 in a preset continuous time window is obtained, where the feature information includes at least one of the following: the depth, the scanning angle, the space curvature, the characteristics of the pulse waveform, the reflectivity and the echo energy are obtained, the characteristic information of each point cloud point in the point cloud data collected in the preset continuous time window is used for identifying and filtering noise points in the point cloud data in the bottom firmware of the distance measuring device, so that the limitation of single pulse sampling is broken through, the inherent time sequence information and the original sampling data of the point cloud points in the point cloud data collected by the distance measuring device are fully utilized, and light noise points, rain fog noise points, crosstalk noise points, wire drawing noise points and the like can be effectively filtered.

Because the ranging device such as a laser radar has the characteristic of continuous high-frequency scanning according to a specific scanning mode, and a scene is continuously scanned according to a specific track, each cloud point (also called a detection point) has obvious time sequence and position information. The points in any time window are mapped onto the scanning track to form a continuous line segment, as shown in fig. 6, which is a typical scanning track of the double prism lidar.

The characteristic information of each cloud point may be obtained based on any suitable method, for example, obtaining the depth of the cloud point collected by the distance measuring device specifically includes: emitting a sequence of light pulses; converting the received return light reflected by the object into an electric signal and outputting the electric signal; sampling the output electrical signal to measure the time difference between transmission and reception of the optical pulse sequence; and receiving the time difference, and calculating the depth value of the cloud point at the current point. The depth values of all the point cloud points collected by the distance measuring device can be obtained by the method. In one example, the detector based ranging device may receive the return light reflected by the object, and the transmitted pulse train may receive a pulse waveform reflected by the object, wherein the characteristics of the pulse waveform include pulse width characteristics, pulse height, pulse area, and the like of the pulse waveform, or may further include other characteristics of the pulse shape. In other examples, a unit for detecting echo energy may be further disposed in the hardware circuit of the ranging apparatus to obtain the echo energy of each cloud point. The reflectivity may be estimated, for example, based on the intensity of the echo energy, or calculated for each cloud point based on other data processing methods known to those skilled in the art.

Then, as shown in fig. 5, in step S502, according to whether the change of the feature information of the continuous point cloud points in the preset continuous time window is continuous, noise points in the point cloud data collected in the preset continuous time window are determined, where the noise points include at least one point cloud point which is discontinuous from the feature information of other point cloud points in the preset continuous time window, and the point cloud points with the abrupt change of the feature information are identified by whether the change of the feature information is continuous, so as to determine whether the point cloud points with the abrupt change are noise points, thereby filtering the noise points in the point cloud data. Optionally, in the point cloud data, one point cloud point is determined to be a noise point before another point cloud point is acquired, so that the processing result is free of delay and high in accuracy, and the process of noise filtering is almost completed in real time along with the point cloud acquisition.

In an example, the method for point cloud noise filtering according to the embodiment of the present invention implements a noise filtering function in an embedded bottom firmware of a distance measuring device based on sampling information in a continuous time window, where the embedded bottom firmware includes a data buffer for storing feature information of point cloud points collected in the continuous time window, where the feature information includes at least one of the aforementioned depth, scanning angle, spatial curvature, feature of pulse waveform, reflectivity, echo energy, and the like, or other feature information of the point cloud points that can be used for noise filtering. The determining noise points 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 or not comprises the following steps: and extracting the characteristic information of the point cloud points collected in the continuous time window from the data buffer area, and determining the noise points in the point cloud data collected in the preset continuous time window. Therefore, the point cloud noise filtering method can realize data storage and calculation only by maintaining a small data buffer (for example, a data buffer with the size of a plurality of KB) in firmware, has low system overhead and strong platform adaptability, and can realize noise filtering and almost delay-free point cloud data output.

In one example, determining noise in the point cloud data acquired within a preset continuous time window according to whether the change of the characteristic information of continuous point cloud points within the preset continuous time window is continuous comprises: determining the isolation degree of the characteristic information of the current point cloud point, wherein the isolation degree is used for determining whether the change of the characteristic information of the current point cloud point is continuous with at least one point cloud point in a preceding sequence time window before the current point cloud point and/or at least one point cloud point in a following sequence time window; determining 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, for example, determining the isolation degree of the feature information of the current point cloud point based on a preset mapping function; and determining whether the cloud point of the current point is a noise point according to a comparison result of the isolation degree of the characteristic information and a set threshold, wherein when the isolation degree of the characteristic information is greater than the set threshold, the cloud point of the current point is determined to be the noise point, wherein the greater the isolation degree is, the greater the deviation of the corresponding characteristic information of the cloud point of the current point from the characteristic information of the cloud points of other points is, and once the isolation degree is greater than the set threshold, the cloud point of the current point can be determined to be the noise point.

For example, when the feature information includes depth, if there is no noise in the continuous point cloud data within a preset continuous time window, the depth change of the continuous point cloud data generally exhibits a characteristic of continuous and regular gradual change with time sequence, considering the characteristics of high frequency scanning of a distance measuring device such as a laser radar (e.g., point cloud interval time of 10 us). Conversely, there are some isolated points in the continuous point cloud data that are not continuous with the depth of most points, and these isolated points may be largely noisy. Based on the above variation characteristics, the "depth isolation degree" of a single-point cloud point can be defined according to the following formula (1),

wherein DI_nThe depth isolation degree defined as the n point cloud point; w is a_fAnd w_bRespectively the sizes of a preamble time window and a subsequent time window; (d)_n-w,d _n-w+1...,d _n...d _n+w-1,d _n+w) Arranging the depth values in the time window according to time sequence; f (-) is a preset mapping function. According to the algorithm design, the higher the "depth isolation" is, the higher the probability of being noise. If "depth isolation" DI_nAnd if the cloud point is larger than the set threshold value T, the nth point cloud point is considered as a noise point.

The preset mapping function can be set reasonably based on the rule of depth change to obtain the 'depth isolation', for example, based on the currentDetermining the preset mapping function by the difference between the depth values of the cloud point and at least one adjacent cloud point, wherein the adjacent cloud point is a point cloud point collected before the current cloud point, i.e. the preorder time window w_fValue 1, subsequent time window w_bIf the value is 0, the preset mapping function can be represented by the following formula (2):

f(d _n-1,d _n)＝d _n-d _n-1formula (2)

The depth value d of the n point cloud point (such as the current point cloud point)_nAnd depth value d of n-1 point cloud point (e.g., adjacent point cloud point collected before the current point cloud point)_n-1Substituting the value into the formula (2), wherein the obtained value can be regarded as the depth isolation degree of the nth point cloud point, comparing the depth isolation degree with a set threshold value, and if the depth isolation degree is greater than the set threshold value, determining that the current point cloud point is a noise point.

The set threshold may be determined reasonably according to the law of depth variation and a priori experience, for example, the set threshold may be determined based on the scanning angular velocity of the ranging apparatus, the maximum filtering included angle, and the depth value of the cloud point at the current point, where the maximum filtering included angle is an included angle between the advancing direction of the optical pulse train emitted by the ranging apparatus and a reference surface, and may be represented by the following formula (3):

where alpha is the scanning angular velocity, theta is the maximum filtering included angle, d_nThe depth value of the cloud point of the current point, that is, the depth value of the cloud point of the nth point.

The included angle theta between the advancing direction of the light pulse sequence emitted by the distance measuring device and the reference surface can be reasonably adjusted according to the actual filtering effect, optionally, the included angle can be in the range of 0-90 degrees, further, the included angle can be in the range of 0-80 degrees, or can be in the range of 0-30 degrees, 0-40 degrees, 0-50 degrees and the like. In the embodiment of the invention, the denoising effects with different intensities can be obtained by adjusting the included angle theta. When theta approaches 90 degrees, almost all point clouds which are not normally incident are filtered out according to the scheme in the embodiment of the invention; as θ approaches 0 °, the scheme according to embodiments of the invention will hardly filter any point cloud. And adjusting theta to obtain filtering of the waveform fusion noise point or other abnormal noise points to different degrees.

Depending on the type of distance measuring device it may be possible to use different set threshold T, in one example, if the scanning angular velocity of the distance measuring device is uniform within the field angle, then for the predetermined maximum filtering angle, α/sin θ 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. Because the point cloud collected by the ranging device when detecting the target scene includes a plurality of point cloud points, the current point cloud point may be any one of the plurality of point cloud points, and each different current point 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 within the field angle, the scanning angular velocity within the entire field angle is a two-dimensional data set related to the zenith angle and the azimuth angle of the scanning, and α/sin θ is the two-dimensional data set for the predetermined angle. A threshold dataset is obtained based on the two-dimensional dataset, thereby filtering the point cloud.

In other examples, if the scanning angular velocity of the distance measuring device is non-uniform within the field angle, the scanning angular velocity within the entire field angle is a two-dimensional data set related to the zenith angle and the azimuth angle of the scanning, the maximum scanning angular velocity of the distance measuring device is found in the two-dimensional data set, the α/sin θ is obtained based on the maximum scanning angular velocity of the distance measuring device and the included angle, and the α/sin θ is applied to obtain a threshold data set, and then applied to the filtering of the cloud point at the current point. The method has the advantages of saving resources, calculating amount and the like, and can play a preset filtering effect on the point cloud.

In addition to determining noise in the point cloud data collected in the preset continuous time window based on the change feature of the depth, noise in the point cloud data collected in the preset continuous time window can be determined based on the change feature of the scanning angle.

In this context, the scan angle of the current point cloud point is an angle between a space vector of the current point cloud point pointing to an adjacent point cloud point adjacent to the current point cloud point and a 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, and the scan angle θ of the current point cloud point n may be calculated based on the following formula, for example_n，

Wherein

The space vector of the point n, that is, the space vector of the cloud point n at the current point,

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

The range finding device such as laser radar has the characteristic of high frequency scanning (for example, the interval time of point cloud is generally 10us), if there is no noise in some continuous point cloud data, the scanning angle should be always at a large value. On the contrary, if the scanning angle suddenly decreases from a large value to a minimum value and suddenly rises back to a large value again after a limited number of minimum values, a plurality of point cloud points at the sudden change are considered to be noise points possibly.

In one example, the feature information further includes a scan angle, and determining noise in the point cloud data acquired in a preset continuous time window according to whether variation of the feature information of continuous point cloud points in the preset continuous time window is continuous includes: the method comprises the steps of obtaining a scanning angle of each point cloud point in a preset continuous time window, wherein the scanning angle of the point cloud point collected before a first time point in the preset continuous time window is above a first threshold scanning angle, and the point cloud point with the scanning angle smaller than the first threshold scanning angle and smaller than a set threshold of the scanning angle is a noise point. The first threshold scan angle and the setting threshold T can be reasonably set according to the prior experience and the scan mode of the ranging device, wherein the first threshold scan angle should be larger than the setting threshold.

Thus, the "scan angle variability" An of a single point cloud point (also referred to as a data point) can be defined according to the following equation (4),

wherein A is_n"scan angle variability" defined as the nth point cloud point; wf and wb are the preamble and the subsequent time window sizes, respectively;

arranging the scanning angles in a time window according to time sequence; f (-) is a preset mapping function. According to the algorithm design, the higher the "scan angle variability" point, the greater the probability of being a noise point. If "scan angular runout" A_nAnd if the cloud point is larger than the set threshold value T, the nth point cloud point is considered as a noise point. The definition of the preset mapping function may be defined in any manner capable of reflecting the scan angle variability, and is not specifically limited herein.

The preset mapping function may be defined by a suitable filtering strategy, according to which it is determined whether a change of characteristic information (e.g. scanning angle) of successive point cloud points within a preset continuous time window is continuous, noise points in the point cloud data acquired within the preset continuous time window are determined, in one example, by the following filtering strategy, including: taking a minimum scanning angle value from a scanning angle of a point cloud point collected in a preamble time window before a current point cloud point, a scanning angle of a point cloud point collected in a subsequent time window after the current point cloud point and a scanning angle of the current point cloud point; and determining whether the point cloud point corresponding to the minimum scanning angle value is a noise point according to the comparison result of the minimum scanning angle value and the set threshold of the scanning angle, wherein when the minimum scanning angle value is smaller than the set threshold of the scanning angle, the point cloud point corresponding to the minimum scanning angle value is determined to be the noise point. Specifically, values of a subsequent time window and a preceding time window may be reasonably set according to actual needs, and optionally, when the preceding time window is 0 and the subsequent time window is 1, the minimum scan angle value is a minimum value of a scan angle of the current point cloud point and a scan angle of an adjacent point cloud point adjacent to the current point cloud point; or, the preamble time window is 1, the subsequent time window is 0, and the minimum scan angle value is a minimum value of a scan angle of the current cloud point and a scan angle of an adjacent cloud point adjacent to the current cloud point in front of the current cloud point.

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

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

Wherein a preamble time window w is taken_f0, subsequent time window w_b＝1，θ _nIs the scan angle, theta, of the cloud point n at the current point_n+1The scan angle of a point cloud point acquired in a subsequent time window of the current point cloud point n may also be regarded as a neighboring point cloud point acquired after the current point cloud point n. Wherein, the smaller the scanning angle is, the scanning angle of the corresponding point cloud point is shown to be mutated compared with the scanning angles of other point cloud points, and the scanning angle is likely to be a noise point, and according to the comparison result of the minimum scanning angle value and the set threshold value T of the scanning angle, whether the point cloud point corresponding to the minimum scanning angle value is a noise point is determined, wherein, when the minimum scanning angle value is smaller than the set threshold value T of the scanning angle, the scanning angle is less than the set threshold value T of the scanning angleAnd when the minimum scanning angle is smaller than 2.5 degrees, determining that the corresponding point cloud point is a noise point.

It is noted that, in this context, the preset continuous time window may include a preceding time window and a following time window and the acquisition time of the current cloud point, and the preceding time window and the following time window may be defined by the acquisition time of the current cloud point (e.g. the nth cloud point), where the preceding time window is a time window before the acquisition time of the current cloud point, and the following time window is a time window after the acquisition time of the current cloud point. The values of the preamble time window and the subsequent time window can be reasonably set according to actual needs.

In another example, the preset mapping function is determined by the following filtering strategy, including: acquiring a scanning angle of each cloud point in a preset continuous time window containing the acquisition time of the current cloud point; determining the maximum number of point cloud points of which the scanning angles acquired in the preset continuous time window are smaller than a set threshold of the scanning angles, wherein the scanning angles of the current point cloud points are smaller than the set threshold of the scanning angles; and determining whether the current point cloud points are noise points according to the comparison result of the maximum number and the preset number, wherein when the maximum number is smaller than the preset number, the current point cloud points are determined to be noise points. According to the filtering policy, the preset mapping function may be equal to the maximum number of the continuous scanning angles including the nth point cloud point in the preset continuous time window smaller than the set threshold T, for example, the size w of the preamble window is taken_f10, subsequent time window size w_bIs 10, setting the threshold T to 10 DEG and the preset number T_θAnd 5, namely when the maximum number of continuous scanning angles containing the n point cloud point is less than 5 and is less than the set threshold value of 10 degrees, determining the n point cloud point as noise. The preset number is set as a filtering condition, so that the number of the point cloud points with mutation can be more accurately determinedIf the number of the point cloud points exceeds the preset number, the noise point may indicate that the change of the scanning angle of the point cloud points is continuous, and whether the point cloud points are noise points or not cannot be determined definitely.

Since each cloud point (also called probe point) carries significant timing and location information. The point in any time window is mapped to the scanning track to form a continuous line segment, the curvature (curvature) of any point cloud point on the scanning track is the rotation rate of a tangent direction angle of a certain point on the curve to the arc length, the curvature is defined by differentiation, the degree of deviation of the curve from a straight line is shown, the numerical value of the bending degree of the curve at the point cloud point is shown, and the larger the curvature is, the larger the bending degree of the curve is. Thus, noise can also be determined by a variation characteristic based on the spatial curvature.

Also considering the characteristics of high frequency scanning of a ranging device such as a laser radar, 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. On the contrary, if an outlier isolated point exists in the continuous point cloud data, so that the curvature change is sharply changed at the point, the isolated point is likely to be a noise point.

In one example, the determining whether the current point cloud point is a noise point according to the comparison result of the isolation degree and a set threshold includes: determining whether the current point cloud point is a noise point according to a comparison result of the isolation degree of the space curvature of the current point cloud point and a set threshold value of the space curvature, wherein when the isolation degree of the space curvature of the current point cloud point is larger than the set threshold value, the current point cloud point is determined to be the noise point.

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

wherein CI_nThe definition is the 'space curvature isolation degree' of the cloud point of the nth point; w is a_fAnd w_bRespectively the sizes of a preamble time window and a subsequent time window; (c)_n-w,c _n-w+1...,c _n...c _n+w-1,c _n+w) Arranging curvature values in a time window according to a time sequence; f (-) is a preset mapping function. According to the algorithm design, the higher the "isolation of spatial curvature" is, the higher the probability of being noise. If "spatial curvature isolation" CI_nAnd if the cloud point is larger than the set threshold value T, the nth point cloud point is considered as a noise point.

Noise in the point cloud data can also be determined based on the variation characteristics of the pulse waveform, where the pulse waveform refers to the pulse waveform of the echo signal received by the ranging device, where the characteristic variation of the pulse waveform may mainly act on noise filtering of multi-echo fusion, and optionally, the characteristics of the pulse waveform include at least one of the pulse width, the pulse height, and the pulse area of the pulse waveform. In this context, the pulse width of the pulse waveform is mainly used for explanation and explanation, but it is understood that the noise filtering based on the changing characteristics of the pulse waveform is not limited to the pulse width.

The characteristics of the pulse waveform comprise the pulse width of the pulse waveform, and the pulse width w is cut-off time t_eAnd time of arrival t_rWherein the arrival time is the time when the echo signal first triggers a time-to-digital converter (TDC) minimum threshold, and the deadline is the time when the echo signal second triggers a time-to-digital converter (TDC) minimum threshold.

In the case of multi-echo fusion, the pulse waveform of the echo signal has the following characteristics: 1. the pulse width is obviously widened; 2. the arrival time of the echo signal is close to the arrival time of a point which completely hits on a near object, and the cut-off time is close to the cut-off time of a point which completely hits on a far object.

Based on the above characteristics, an "abnormal spread width" of a pulse width of a pulse waveform may be defined, and in one example, the abnormal spread width of a point cloud point collected within the preset continuous time window may be determined according to a preset mapping function; and determining noise points in the point cloud data collected in the preset continuous time window according to the abnormal broadening widths, wherein the noise points comprise point cloud points with the abnormal broadening degrees larger than a set threshold value.

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

wherein S is_nThe abnormal spread width is defined as the 'abnormal spread width' of the n point cloud point; w is a_fAnd w_bRespectively the sizes of a preamble time window and a subsequent time window;

arranging arrival time and cut-off time in a time window according to time sequence; f (-) is a preset mapping function. According to the algorithm design, the larger the abnormal spread width is, the more probable the cloud point is noise. If "abnormal spreading width" S_nAnd if the cloud point is larger than the set threshold value T, the nth point cloud point is considered as a noise point.

The preset mapping function of the cloud point of the current point (for example, the nth cloud point) is set according to whether the pulse width, the arrival time and the cutoff time of the cloud point of the continuous point of the preset continuous time window meet preset filtering conditions, wherein when the preset filtering conditions are met, the abnormal broadening degree of the cloud point of the current point determined according to the preset mapping function is greater than a set threshold, and when the preset filtering conditions are not met, the abnormal broadening degree of the cloud point of the current point determined according to the preset mapping function is less than the set threshold.

In one example, when the pulse width, the arrival time, and the cutoff time satisfy the preset filtering condition, the current point cloud point is determined to be noise, and the preset filtering condition includes at least one of the following conditions:

first time point t in preamble time window of cloud point of current point₁Pulse width w of echo signal_t1Pulse width w at a previous time instant compared to the first time instant_t1-1An abrupt broadening occurs, wherein the abrupt broadening is greater than a first pulse width set threshold, i.e.

Second point in time t within a subsequent time window of the current point cloud point₂Pulse width w of echo signal_t2Pulse width w at a previous time instant compared to the second time instant_t2-1An abrupt reduction occurs, wherein the abrupt reduction is greater than a first pulse width set threshold, i.e.

The arrival time in the time window between the first time point and the second time point is within a first threshold from the preamble arrival time of the first time point and within a second threshold from the postsequence cutoff time of the second time point, that is, the arrival time of the echo signal is close to the arrival time of a point which completely hits on a near object and the cutoff time is close to the cutoff time of a point which completely hits on a far object, or the arrival time in the time window between the first time point and the second time point is within a third threshold from the preamble cutoff time of the first time point and within a fourth threshold from the postsequence 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.

In one example, the preamble window size w may be taken_f10, subsequent time window size w_bIs 10; if the arrival time and the deadline time in the time window satisfy the preset filtering condition, the preset mapping function f (·) may output a first numerical value, which is the abnormal spreading width of the cloud point at the current point, so that the first numerical value is greater than the set threshold, and when the preset filtering condition is not satisfied, a second numerical value, which is the abnormal spreading width of the cloud point at the current point, may be output according to the preset mapping function, where the second numerical value is less than the set threshold, for example, the first numerical value is 1, and the second numerical value is 0, or other suitable numerical values. If the output of f (·) is 1, the nth point cloud point is considered as noise; and if the output is 0, the n point cloud point is considered as a normal point.

And noise filtering can be realized based on the change characteristic of the reflectivity, and the method is particularly used for noise filtering of multi-echo fusion.

Under the currently applied reflectivity calculation model, the abnormally broadened waveform will cause the reflectivity of the point cloud point to be calculated as a very small value. Considering the characteristics of high frequency scanning of a distance measuring device such as a laser radar (e.g., the point cloud interval time is 10us), if there is no noise in some continuous point cloud data, the reflectivity thereof is generally stable at a certain large value. If there are consecutive points within a time window whose reflectivity suddenly decreases from a large value to a minimum value and suddenly increases back to a large value again after a limited number of minimum values, the consecutive points are considered as noise.

In one example, the characteristic information includes reflectivity, and determining noise in the point cloud data acquired in a preset continuous time window according to whether variation of the characteristic information of continuous point cloud points in the preset continuous time window is continuous comprises: and the reflectivity of the continuous point cloud points in any time window in the preset continuous time window is reduced from the first reflectivity to the second reflectivity, and the reflectivity of the point cloud points is increased from the second reflectivity to the third reflectivity according to a time sequence, 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 indicates that the reflectivity changes discontinuously of the point cloud points of the continuous point cloud points and the gas in the preset continuous time window, so that the noise points are determined to be the noise points, and the noise points in the point cloud data are identified. Optionally, the second reflectivity may be smaller than a set threshold of reflectivity, which is reasonably set according to a priori experience, wherein the first reflectivity and the third reflectivity may be the same reflectivity or different reflectivities.

Based on the above-described change characteristics of the reflectance, "reflectance mutation degree" is defined, wherein the "reflectance mutation degree" reflects the degree of mutation of the reflectance of the cloud point.

In one example, the characteristic information includes reflectivity, and determining noise in the point cloud data acquired in a preset continuous time window according to whether variation of the characteristic information of continuous point cloud points in the preset continuous time window is continuous comprises: determining the reflectivity abrupt change degree of the point cloud points collected in the preset continuous time window according to a preset mapping function; and determining noise points in the point cloud data collected in the preset continuous time window according to the reflectivity mutation degree, wherein the noise points comprise point cloud points with the reflectivity mutation degree larger than a set threshold value.

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

wherein R is_nDefined as the "reflectivity bump" of the nth point cloud point; w is a_fAnd w_bRespectively the sizes of a preamble time window and a subsequent time window;

arranging the scanning angles in a time window according to time sequence; f (-) is the mapping function. According to the algorithm design, points with a higher "reflectivity abrupt" are more likely to be noise points. If "degree of reflectivity sudden change" R_nAnd if the cloud point is larger than the set threshold value T, the nth point cloud point is considered as a noise point.

In one example, the preset mapping function of the current point cloud point is set according to whether the reflectivity of the continuous point cloud point of the preset continuous time window meets a preset filtering condition, wherein when the preset filtering condition is met, the reflectivity sudden change degree of the current point cloud point determined according to the preset mapping function is greater than a set threshold, and when the preset filtering condition is not met, the reflectivity sudden change degree of the current point cloud point determined according to the preset mapping function is less than the set threshold.

In one example, the preset continuous time window includes a preamble time window and a subsequent time window, wherein the preamble time window is located before the acquisition time of the current cloud point, the subsequent time window is located after the acquisition time of the current cloud point, and the preset filtering condition includes the following condition:

at a first point in time t of said preamble time window₁Reflectivity r of the point cloud point of the point collection_t1Reduced to a first threshold reflectivityAnd at said first point in time t₁Reflectivity r of previously acquired point cloud points_t1The reflectivity r of the point cloud point collected at the first time point_t1-1Is greater than a set threshold value T_rFor example, if the first threshold reflectivity is 3, then r_t1Less than 3, and r_t1-1-r _t1Greater than T_rIndicating that the reflectivity of the cloud point is suddenly reduced at the starting point at the first time t 1;

at a second point in time t of the subsequent time window₂Reflectivity r of the point cloud point of the point collection_t2Below the first threshold reflectivity and the second point in time t₂The reflectivity r of the later collected point cloud point_t2+1Increase and reflectivity r of a point cloud point acquired after the second point in time_t2+1And the reflectivity r of the point cloud point collected at the second time point_t2Is greater than a set threshold value T_r(ii) a For example, if the first threshold reflectivity is 3, then r_t2Less than 3, and r_t2+1-r _t2Greater than T_rIt indicates that the reflectivity of the point cloud point rises steeply from below 3 at the second time t2, and thus, it indicates that the point cloud point collected between the first time point and the second time point is likely to be noise.

Further, the first time point t₁To the second time point t₂The reflectivity of all the internally collected point cloud points is less than the first threshold reflectivity, and the number of all the point cloud points collected from the first time point to the second time point is less than a threshold number, for example, the first time point t₁To the second time point t₂The reflectivity of all internally collected point cloud points is less than 3, and if the threshold number is 10, t is₂-t ₁Less than 10, so that by this filtering condition it can be determined that the reduction in reflectivity of the point cloud collected between the first and second points in time is not due to the reflectivity of the probe itself. The first threshold reflectivity and the threshold number may beIt is reasonable to set according to a priori experience, and is not particularly limited herein.

Further, the acquisition time of the cloud point at the current point is between the first time point and the second time point, and the reflectivity of the cloud point at the current point is also smaller than the first threshold reflectivity, so that the cloud point at the current point can be determined to be a noise point.

In one example, the preamble window size w may be taken_f10, subsequent time window size w_bIs 10; if the preset filtering condition of the reflectivity is met in the time window, the preset mapping function f (·) may output a first numerical value, which is the "reflectivity abrupt change" of the cloud point at the current point, and is larger than the set threshold, and when the preset filtering condition is not met, a second numerical value, which is the "reflectivity abrupt change" of the cloud point at the current point, may be output according to the preset mapping function, and the second numerical value is smaller than the set threshold, for example, the first numerical value is 1, and the second numerical value is 0, or other suitable numerical values. If the output of f (·) is 1, the nth point cloud point is considered as noise; and if the output is 0, the n point cloud point is considered as a normal point.

In the above, four obvious features of point cloud data in a continuous time window are mainly used as noise filtering bases, and are respectively 1) change features based on depth; 2) a change characteristic based on a scan angle; 3) a change characteristic based on reflectivity; 4) the method for filtering noise of point cloud of the present invention is explained and illustrated based on the pulse waveform change characteristics, but it should be understood that:

1. the four strategies can work independently and can be combined randomly to form a composite filtering strategy, namely, the composite filtering can be carried out through any one, any two, any three or all four strategies;

2. although only for each strategy of the present invention, one or two simpler illustrative examples are given above. In fact, there are various choices and combinations for the selection of the preset mapping function f (-), the setting of the size of the time window, the setting of the threshold value of various auxiliary judgments and the final judgment strategy. Based on the logic, various filtering strategies can be developed according to actual requirements.

3. The filtering strategy can be artificially designed through a first law, and can also be used as a characteristic input model for machine learning to train on a marked data set to obtain a noise classifier with good performance.

In addition to the above-mentioned characteristic information, the point cloud may have other varying characteristics in the time window sequence. Such as: if the machine has the capability of accurately detecting the echo energy, the variation of the echo energy in the time window sequence can also be used as a feature for filtering the noise, for example, the noise may have other variation features in the time window sequence, and these features can be obtained by sampling through an additionally designed hardware circuit. Such as: if the hardware circuit has the capability of accurately detecting the echo energy, the change of the echo energy in the time window sequence is also used as a characteristic for filtering noise points. In other examples, the input of the noise filtering strategy may contain externally input auxiliary information in addition to the pulse sampling information within the time window. For example, whether the current weather is sunny or rainy, such as whether the radar is applied to an indoor environment (drawing noise is easy to occur) or an outdoor environment, and the like, noise filtering of the point cloud data is assisted through the input of the auxiliary information.

In an example, the point cloud noise filtering method according to the embodiment of the present invention is implemented in bottom firmware of a distance measuring device, such as an FPGA (field programmable gate array), and can more accurately distinguish noise points from non-noise points by using original sampling data (e.g., the feature information of the point cloud points acquired by the distance measuring device), and the probability of false judgment and missed judgment is much less than that of the existing noise filtering scheme based on an upper-layer algorithm of a spatial coordinate relationship in actual use.

The invention utilizes the inherent characteristic of high-frequency scanning according to a specific scanning mode of a distance measuring device such as a laser radar to analyze and extract a plurality of obvious characteristics of a plurality of data points of the noise points in a continuous time window as a noise filtering basis, and a corresponding noise filtering strategy is formulated to identify and filter the noise points. Therefore, the method of the embodiment of the invention breaks through the limitation of single pulse sampling, fully utilizes the inherent time sequence information and original sampling data of the detection point, and can effectively filter the light noise, the rain fog noise, the crosstalk noise, the wire drawing noise and the like which cannot be identified by the existing scheme.

In one example, the method of point cloud noise filtering of the present invention can be further implemented in a display unit, wherein the display unit is communicatively connected to the ranging device for acquiring raw sampling data collected by the ranging device. Or the display unit can be used for displaying the point cloud data after the noise filtering processing of the distance measuring device or displaying the point cloud data without the noise filtering processing.

For the point cloud point determined as a noise point by the above method, the point cloud point may be processed according to a method, for example, when the current point cloud point is determined as a noise point, the current point cloud point is filtered, for example, the current point cloud point determined as a noise point is set to have a flag bit of 0, that is, directly filtered, and optionally, the point cloud point filtering is performed during the process of the distance measuring device acquiring the point cloud point, so that the distance measuring device may directly output point cloud data almost without a noise point. In another example, the point cloud points determined as noise points are marked, the marked point cloud points are assigned with special values or the current point cloud points are directly filtered, the special values are distinguished from other point cloud points without noise points, and then the processing mode is determined by an upper-layer algorithm (namely, an upper-layer application), such as an object segmentation recognition algorithm, a three-dimensional reconstruction algorithm and the like. It should be noted that, in the embodiment of the present invention, the value after processing is used only when data is sent to the upper layer application, and the original data before filtering is always reserved in the embodiment of the present invention as a reference for the next filtering algorithm.

In the following, the structure of a distance measuring device in the embodiments of the present invention, which includes a laser radar, is exemplarily described in more detail with reference to fig. 7 and 8, and the distance measuring device is merely an example, and other suitable distance measuring devices may be applied to the present application. The distance measuring device is used for executing the method for filtering the noise of the point cloud in the embodiment.

The scheme provided by each embodiment of the invention can be applied to a distance measuring device, and the distance measuring device can be electronic equipment such as a laser radar, laser distance measuring equipment and the like. In one embodiment, the ranging device is used to sense external environmental information, such as distance information, orientation information, reflected intensity information, velocity information, etc. of environmental targets. In one implementation, the ranging device may detect the distance of the probe to the ranging device by measuring the Time of Flight (TOF), which is the Time-of-Flight Time, of light traveling between the ranging device and the probe. Alternatively, the distance measuring device may detect the distance from the probe to the distance measuring device by other techniques, such as a distance measuring method based on phase shift (phase shift) measurement or a distance measuring method based on frequency shift (frequency shift) measurement, which is not limited herein.

For ease of understanding, the following describes an example of the ranging operation with reference to the ranging apparatus 100 shown in fig. 7.

Illustratively, the distance measuring device may include a transmitting module, a receiving module and a temperature control system, wherein the transmitting module is used for emitting light pulses; the receiving module is used for receiving at least part of the light pulse reflected back by the object and determining the distance of the object relative to the distance measuring device according to the received at least part of the light pulse.

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

The transmit circuit 110 may emit a train of light pulses (e.g., a train of laser pulses). The receiving circuit 120 may receive the optical pulse train reflected by the detected object, that is, obtain the pulse waveform of the echo signal through the optical pulse train, perform photoelectric conversion on the optical pulse train to obtain an electrical signal, process the electrical signal, and output the electrical signal to the sampling circuit 130. The sampling circuit 130 may sample the electrical signal to obtain a sampling result. The arithmetic circuit 140 may determine the distance, i.e., the depth, between the ranging device 100 and the detected object based on the sampling result of the sampling circuit 130.

Optionally, the distance measuring apparatus 100 may further include a control circuit 150, and the control circuit 150 may implement control of other circuits, for example, may control an operating time of each circuit and/or perform parameter setting on each circuit, and the like.

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 to detect, the embodiments of the present application are not limited thereto, and the number of any one of the transmitting circuit, the receiving circuit, the sampling circuit and the arithmetic circuit may be at least two, and the at least two light beams are emitted in the same direction or in different directions respectively; the at least two light paths may be emitted simultaneously or at different times. In one example, the light emitting chips in the at least two transmitting circuits are packaged in the same module. For example, each transmitting circuit comprises a laser emitting chip, and die of the laser emitting chips in the at least two transmitting circuits are packaged together and accommodated in the same packaging space.

In some implementations, in addition to the circuit shown in fig. 7, the distance measuring apparatus 100 may further include a scanning module for emitting at least one light pulse sequence (e.g., a laser pulse sequence) emitted from the emitting circuit with a changed propagation direction so as to scan the field of view. Illustratively, the scan area of the scan module within the field of view of the ranging device increases over time.

Here, a module including the transmission circuit 110, the reception circuit 120, the sampling circuit 130, and the operation circuit 140, or a module including the transmission circuit 110, the reception circuit 120, the sampling circuit 130, the operation circuit 140, and the control circuit 150 may be referred to as a ranging module, which may be independent of other modules, for example, a scanning module.

The distance measuring device can adopt a coaxial light path, namely the light beam emitted by the distance measuring device and the reflected light beam share at least part of the light path in the distance measuring device. For example, at least one path of laser pulse sequence emitted by the emitting circuit is emitted by the scanning module after the propagation direction is changed, and the laser pulse sequence reflected by the detector is emitted to the receiving circuit after passing through the scanning module. Alternatively, the distance measuring device may 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 one embodiment of the ranging device of the present invention using coaxial optical paths.

The ranging apparatus 200 comprises a ranging module 210, the ranging module 210 comprising an emitter 203 (which may comprise the transmitting circuitry described above), a collimating element 204, a detector 205 (which may comprise the receiving circuitry, sampling circuitry and arithmetic circuitry described above) and a path-altering element 206. The distance measuring module 210 is configured to emit a light beam, receive return light, and convert the return light into an electrical signal. Wherein the emitter 203 may be configured to emit a sequence of light pulses. In one embodiment, the transmitter 203 may emit a sequence of laser pulses. Alternatively, the laser beam emitted by the emitter 203 is a narrow bandwidth beam having a wavelength outside the visible range. The collimating element 204 is disposed on an emitting light path of the emitter, and is configured to collimate the light beam emitted from the emitter 203, and collimate the light beam emitted from the emitter 203 into parallel light to be emitted to the scanning module. The collimating element is also for converging at least a portion of the return light reflected by the detector. The collimating element 204 may be a collimating lens or other element capable of collimating a light beam.

In the embodiment shown in fig. 8, the transmit and receive optical paths within the distance measuring device are combined by the optical path changing element 206 before the collimating element 204, so that the transmit and receive optical paths can share the same collimating element, making the optical path more compact. In other implementations, the emitter 203 and the detector 205 may use respective collimating elements, and the optical path changing element 206 may be disposed in the optical path after the collimating elements.

In the embodiment shown in fig. 8, since the beam aperture of the light beam emitted from the emitter 203 is small and the beam aperture of the return light received by the distance measuring device is large, the optical path changing element can adopt a small-area mirror to combine the emission optical path and the reception optical path. In other implementations, the optical path changing element may also be a mirror with a through hole, wherein the through hole is used for transmitting the outgoing light from the emitter 203, and the mirror is used for reflecting the return light to the detector 205. Therefore, the shielding of the bracket of the small reflector to the return light can be reduced in the case of adopting the small reflector.

In the embodiment shown in fig. 8, the optical path altering element is offset from the optical axis of the collimating element 204. In other implementations, the optical path altering element may also be located on the optical axis of the collimating element 204.

The ranging device 200 also includes a scanning module 202. The scanning module 202 is disposed on the emitting light path of the distance measuring module 210, and the scanning module 202 is configured to change the transmission direction of the collimated light beam 219 emitted by the collimating element 204, project the collimated light beam to the external environment, and project the return light beam to the collimating element 204. The return light is converged by the collimating element 204 onto the detector 205.

In one 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, refracting, diffracting, etc. the optical element includes at least one light refracting element having non-parallel exit and entrance faces, for example. For example, the scanning module 202 includes a lens, mirror, prism, galvanometer, grating, liquid crystal, Optical Phased Array (Optical Phased Array), or any combination thereof. In one example, at least a portion of the optical element is moved, for example, by a driving module, and the moved optical element can reflect, refract, or diffract the light beam to different directions at different times. In some embodiments, multiple optical elements of the scanning module 202 may rotate or oscillate about a common axis 209, with each rotating or oscillating optical element serving to constantly change the direction of propagation of an incident beam. In one embodiment, the multiple optical elements of the scanning module 202 may rotate at different rotational speeds or oscillate at different speeds. In another embodiment, at least some of the optical elements of the scanning module 202 may rotate at substantially the same rotational speed. In some embodiments, the multiple optical elements of the scanning module may also be rotated about 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 in the same direction, or in different directions, without limitation.

In one embodiment, the scanning module 202 includes a first optical element 214 and a driver 216 coupled to the first optical element 214, the driver 216 configured to drive the first optical element 214 to rotate about the rotation axis 209, such that the first optical element 214 redirects the collimated light beam 219. The first optical element 214 projects the collimated beam 219 into different directions. In one embodiment, the angle between the direction of the collimated beam 219 after it is altered by the first optical element and the axis of rotation 209 changes as the first optical element 214 is rotated. 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 having a thickness that varies along at least one radial direction. In one embodiment, the first optical element 214 comprises a wedge angle prism that refracts the collimated beam 219.

In one embodiment, the scanning module 202 further comprises a second optical element 215, the second optical element 215 rotating around a rotation axis 209, the rotation speed of the second optical element 215 being 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 coupled 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 may be driven by the same or different drivers, such that the first optical element 214 and the second optical element 215 rotate at different speeds and/or turns, thereby projecting the collimated light beam 219 into different directions in the ambient space, which may scan a larger spatial range. 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 rotation speed of the first optical element 214 and the second optical element 215 can be determined according to the region and the pattern expected to be scanned in the actual application. The

drives

216 and 217 may include motors or other drives.

In one embodiment, second optical element 215 includes a pair of opposing non-parallel surfaces through which the light beam passes. In one embodiment, second optical element 215 includes a prism having a thickness that varies along at least one radial direction. In one embodiment, second optical element 215 comprises a wedge angle prism.

In one embodiment, the scan module 202 further comprises a third optical element (not shown) and a driver for driving the third optical element to move. Optionally, the third optical element comprises a pair of opposed non-parallel surfaces through which the light beam passes. In one embodiment, the third optical element comprises a prism having a thickness that varies along at least one radial direction. In one embodiment, the third optical element comprises a wedge angle prism. At least two of the first, second and third optical elements rotate at different rotational speeds and/or rotational directions.

In one embodiment, the scanning module comprises 2 or 3 photorefractive elements arranged in sequence on an outgoing light path of the optical pulse sequence. Optionally, at least 2 of the photorefractive elements in the scanning module rotate during scanning to change the direction of the sequence of light pulses.

The scanning module has different scanning paths at least partially different times, and the rotation of each optical element in the scanning module 202 may project light in different directions, such as the direction of the projected light 211 and the direction 213, so as to scan the space around the distance measuring device 200. 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 the opposite direction to the projected light 211. The return light 212 reflected by the object 201 passes through the scanning module 202 and then enters the collimating element 204.

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

In one embodiment, each optical element is coated with an antireflection coating. 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 coated on a surface of a component in the distance measuring device, which is located on the light beam propagation path, or a filter is arranged on the light beam propagation path, and is used for transmitting at least a wave band in which the light beam emitted by the emitter is located and reflecting other wave bands, so as to reduce noise brought to the receiver by ambient light.

In some embodiments, the transmitter 203 may include a laser diode through which laser pulses in the order of nanoseconds are emitted. Further, the laser pulse reception time may be determined, for example, by detecting the rising edge time and/or the falling edge time of the electrical signal pulse. In this manner, the ranging apparatus 200 may calculate TOF using the pulse reception time information and the pulse emission time information, thereby determining the distance of the probe 201 to the ranging apparatus 200. The distance and orientation detected by ranging device 200 may be used for remote sensing, obstacle avoidance, mapping, modeling, navigation, and the like.

In some embodiments, the ranging device further comprises one or more processors, one or more memory devices, the one or more processors operating collectively or individually. Optionally, the ranging device may further include at least one of an input device (not shown), an output device (not shown), and an image sensor (not shown), which are interconnected by a bus system and/or other form of connection mechanism (not shown).

The storage means, i.e. the memory, is used for storing a memory for processor executable instructions, e.g. for the corresponding steps and program instructions present in the method for point cloud noise filtering for implementing a ranging apparatus according to an embodiment of the invention. May include one or more computer program products that may include various forms of computer-readable storage media, such as volatile memory and/or non-volatile memory. The volatile memory may include, for example, Random Access Memory (RAM), cache memory (cache), and/or the like. The non-volatile memory may include, for example, Read Only Memory (ROM), hard disk, flash memory, etc.

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, a touch screen, and the like.

The output device may output various information (e.g., images or sounds) to the outside (e.g., a user), and may include one or more of a display, a speaker, and the like, for outputting the point cloud points of non-noise points collected by the ranging device as images or videos.

A communication interface (not shown) is used for communication between the ranging apparatus and other devices, including wired or wireless communication. The ranging device may access a wireless network based on a communication standard, such as WiFi, 2G, 3G, 4G, 5G, or a combination thereof. In one exemplary embodiment, the communication interface receives a broadcast signal or broadcast associated information from an external broadcast management system via a broadcast channel. In one exemplary embodiment, the communication interface further comprises a Near Field Communication (NFC) module to facilitate short-range communication. For example, the NFC module may 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), a Graphics Processing Unit (GPU), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other form of processing unit having data processing capabilities and/or instruction execution capabilities, and may control other components in the ranging apparatus to perform desired functions. The processor can execute the instructions stored in the storage device to execute the method for noise filtering of the point cloud of the ranging apparatus described herein, which is described in the foregoing embodiments and is not repeated herein. For example, a processor can include one or more embedded processors, processor cores, microprocessors, logic circuits, hardware Finite State Machines (FSMs), Digital Signal Processors (DSPs), or a combination thereof. In this embodiment, the processor comprises a Field Programmable Gate Array (FPGA), wherein the arithmetic circuitry of the ranging device may be part of the Field Programmable Gate Array (FPGA). In one example, the method for performing the point cloud noise filtering in the foregoing embodiment is implemented in the bottom firmware of the ranging apparatus, such as a field programmable gate array FPGA, so that the original sampling data (e.g., the characteristic information of the point cloud collected by the ranging apparatus herein) can be used to more accurately distinguish noise points from non-noise points, and the probability of false judgment and missed judgment is much less than that of the existing noise filtering scheme based on the spatial coordinate relationship in practical use.

The ranging apparatus comprises one or more processors, working together or separately, a memory for storing program instructions; the processor is configured to execute the memory-stored program instructions, and when executed, the processor is configured to:

and determining noise points in the point cloud data collected in a 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 or not, wherein the noise points comprise at least one point cloud point which is discontinuous with the characteristic information of other point cloud points in the preset continuous time window. Optionally, in the point cloud data, one point cloud point is determined to be a noise point before another point cloud point is acquired, so that the processing result is free of delay and high in accuracy, and the process of noise filtering is almost completed in real time along with the point cloud acquisition.

In an example, the method for point cloud noise filtering according to the embodiment of the present invention implements a noise filtering function in an embedded bottom firmware of a distance measuring device based on sampling information in a continuous time window, where the embedded bottom firmware includes a data buffer for storing feature information of point cloud points collected in the continuous time window, where the feature information includes at least one of the aforementioned depth, scanning angle, spatial curvature, feature of pulse waveform, reflectivity, echo energy, and the like, or other feature information of the point cloud points that can be used for noise filtering. The determining noise points 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 or not comprises the following steps: and extracting the characteristic information of the point cloud points collected in the continuous time window from the data buffer area, and determining the noise points in the point cloud data collected in the preset continuous time window. Therefore, the ranging device can realize the storage and calculation of data only by maintaining a small data buffer (for example, a data buffer with the size of a plurality of KB) in firmware, has low system overhead and strong platform adaptability, and can realize the noise filtering and the almost delay-free point cloud data output.

In one example, the processor is specifically configured to, when determining noise points in the point cloud data acquired within a preset continuous time window according to whether a change in the feature information of continuous point cloud points within the preset continuous time window is continuous,: determining the isolation degree of the characteristic information of the current point cloud point, wherein the isolation degree is used for determining whether the change of the characteristic information of the current point cloud point is continuous with at least one point cloud point in a preorder time window before the current point cloud point and/or at least one point cloud point in a subsequent time window; and determining whether the cloud point of the current point is a noise point according to the isolation degree of the characteristic information of the cloud point of the current point.

In one example, when the processor determines whether a current point cloud point is noisy according to an isolation degree of the feature information of the current point cloud point, the processor is specifically configured to: determining the isolation degree of the characteristic information of the cloud point of the current point based on a preset mapping function; and determining whether the current point cloud point is a noise point according to a comparison result of the isolation degree of the characteristic information and a set threshold, wherein when the isolation degree of the characteristic information is greater than the set threshold, the current point cloud point is determined to be the noise point.

In one example, the feature information includes a depth, and the preset mapping function is determined based on a difference between depth values of the current point cloud point and at least one neighboring point cloud point, the neighboring point cloud point being a point cloud point acquired before the current point cloud point. Optionally, the set threshold is determined based on a scanning angular velocity of the ranging device, a maximum filtering included angle, and a depth value of the cloud point at the current point, where the maximum filtering included angle is an included angle between an advancing direction of the light pulse sequence emitted by the ranging device and a reference plane.

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

In one example, the processor is specifically configured to, when determining noise points in the point cloud data acquired within a preset continuous time window according to whether a change in the feature information of continuous point cloud points within the preset continuous time window is continuous,: the method comprises the steps of obtaining a scanning angle of each point cloud point in a preset continuous time window, wherein the scanning angle of the point cloud point collected before a first time point in the preset continuous time window is above a first threshold scanning angle, and the point cloud point with the scanning angle smaller than the first threshold scanning angle and smaller than a set threshold of the scanning angle is a noise point.

In one example, the processor is specifically configured to, when determining noise points in the point cloud data acquired within a preset continuous time window according to whether a change in the feature information of continuous point cloud points within the preset continuous time window is continuous,:

taking a minimum scanning angle value from a scanning angle of a point cloud point collected in a preamble time window before a current point cloud point, a scanning angle of a point cloud point collected in a subsequent time window after the current point cloud point and a scanning angle of the current point cloud point;

and determining whether the point cloud point corresponding to the minimum scanning angle value is a noise point according to the comparison result of the minimum scanning angle value and the set threshold of the scanning angle, wherein when the minimum scanning angle value is smaller than the set threshold of the scanning angle, the point cloud point corresponding to the minimum scanning angle value is determined to be the noise point.

Optionally, when the preamble time window is 0 and a subsequent time window is 1, the minimum scan angle value is a minimum value of a scan angle of the current cloud point and a scan angle of an adjacent cloud point adjacent to the current cloud point; or, the preamble time window is 1, the subsequent time window is 0, and the minimum scan angle value is a minimum value of a scan angle of the current cloud point and a scan angle of an adjacent cloud point adjacent to the current cloud point in front of the current cloud point.

In one example, the processor is specifically configured to, when determining noise points in the point cloud data acquired within a preset continuous time window according to whether a change in the feature information of continuous point cloud points within the preset continuous time window is continuous,: acquiring a scanning angle of each cloud point in a preset continuous time window containing the acquisition time of the current cloud point; determining the maximum number of the point cloud points of which the collected scanning angles are smaller than a set threshold value of the scanning angles in the preset continuous time window; and determining whether the current point cloud points are noise points according to the comparison result of the maximum number and the preset number, wherein when the maximum number is smaller than the preset number, the current point cloud points are determined to be noise points.

In one example, the feature information includes a spatial curvature, and the processor is specifically configured to, when determining whether the current point cloud point is a noise point according to a comparison result between the isolation degree and a set threshold: and determining whether the current point cloud point is a noise point according to a comparison result of the isolation degree of the space curvature of the current point cloud point and a set threshold, wherein when the isolation degree of the space curvature of the current point cloud point is greater than the set threshold, the current point cloud point is determined to be the noise point.

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

In one example, the processor is specifically configured to, when determining noise points in the point cloud data acquired within a preset continuous time window according to whether a change in the feature information of continuous point cloud points within the preset continuous time window is continuous,: determining the abnormal spread width of the point cloud points collected in the preset continuous time window according to a preset mapping function; and determining noise points in the point cloud data collected in the preset continuous time window according to the abnormal broadening widths, wherein the noise points comprise point cloud points with the abnormal broadening degrees larger than a set threshold value.

The preset mapping function of the cloud point at the current point is set according to whether the pulse width, the arrival time and the cut-off time of the cloud point at the continuous point of the preset continuous time window meet preset filtering conditions, wherein when the preset filtering conditions are met, the abnormal broadening degree of the cloud point at the current point determined according to the preset mapping function is larger than a set threshold, and when the preset filtering conditions are not met, the abnormal broadening degree of the cloud point at the current point determined according to the preset mapping function is smaller than the set threshold.

the method comprises the steps that the pulse width of an echo signal at a first time point in a preamble time window of a cloud point at a current point is increased compared with the pulse width at the previous moment of the first time point, wherein the increased pulse width is larger than a first pulse width setting threshold;

the pulse width of an echo signal at a second time point in a subsequent time window of a cloud point of a current point is suddenly reduced compared with the pulse width at the previous moment of the second time point, wherein the sudden reduction is larger than a first pulse width setting threshold value;

an arrival time within a time window between the first time point and the second time point is within a first threshold from a preamble arrival time of the first time point and a cutoff time within the time window is within a second threshold from a subsequent cutoff time of the second time point, or the cutoff time within the time window between the first time point and the second time point is within a third threshold time from the preamble cutoff time of the first time point and the arrival time within the time window is within a fourth threshold time from the subsequent arrival time of the second time point;

In one example, the characteristic information includes reflectivity, the processor is further configured to: and the reflectivity of the continuous point cloud points in any time window in the preset continuous time window is reduced from the first reflectivity to the second reflectivity, and the reflectivity of the point cloud points is increased from the second reflectivity to the third reflectivity according to the time sequence, 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.

In one example, the characteristic information comprises reflectivity, and the processor is specifically configured to, when determining noise in the point cloud data acquired within a preset continuous time window, determine whether a change in the characteristic information of successive point cloud points within the preset continuous time window is continuous: determining the reflectivity abrupt change degree of the point cloud points collected in the preset continuous time window according to a preset mapping function; and determining noise points in the point cloud data collected in the preset continuous time window according to the reflectivity mutation degree, wherein the noise points comprise point cloud points with the reflectivity mutation degree larger than a set threshold value.

Optionally, the preset mapping function of the current point cloud point is set according to whether the reflectivity of the continuous point cloud point of the preset continuous time window meets a preset filtering condition, wherein when the preset filtering 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 filtering condition is not met, the reflectivity mutation degree of the current point cloud point determined according to the preset mapping function is less than the set threshold.

Illustratively, the preset continuous time window includes a preamble time window and a subsequent time window, wherein the preamble time window is located before the acquisition time of the current cloud point, the subsequent time window is located after the acquisition time of the current cloud point, and the preset filtering condition includes the following condition:

the reflectivity of the point cloud point collected at the first time point of the preamble time window is reduced to be lower than a first threshold reflectivity, and the difference value between the reflectivity of the point cloud point collected before the first time point and the reflectivity of the point cloud point collected at the first time point is larger than a set threshold;

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

the reflectivity of all the point cloud points collected from the first time point to the second time point is smaller than the first threshold reflectivity, and the number of all the point cloud points collected from the first time point to the second time point is smaller than the threshold number; the acquisition time of the cloud point at the current point is between the first time point and the second time point.

In one example, the ranging apparatus further includes: hardware circuitry (not shown) for sampling the echo energy.

In the following, referring to fig. 9, a structure of a ranging system in an embodiment of the present invention is exemplarily described in more detail, where the ranging system 900 includes a ranging apparatus 901 and a display unit 902, where one of the ranging apparatus 901 and the display unit 902 is used to implement the relevant steps of the method for filtering noise of point cloud described above.

In one example, the ranging apparatus 901 includes a memory and a processor for storing executable instructions, the processor for executing the instructions stored in the memory, causing the processor to perform the steps associated with the method of point cloud noise filtering.

In this embodiment, the specific structure and description of the distance measuring device 901 may refer to the structures in fig. 7 and fig. 8, which are not described herein again.

In one example, the display unit 902 is to: acquiring point cloud data output by the distance measuring device, wherein point cloud points which are determined to be noise points in the point cloud data are marked by flag bits; and displaying the point cloud data after the noise points are filtered or displaying the point cloud data containing the noise points according to an instruction input by a user.

The display unit may include a display, an input device, and a memory and processor, etc. The memory is used for storing a memory of processor executable instructions, such as corresponding steps and program instructions in a method for point cloud noise filtering existing for implementing a ranging apparatus according to an embodiment of the present invention. May include one or more computer program products that may include various forms of computer-readable storage media, such as volatile memory and/or non-volatile memory. The volatile memory may include, for example, Random Access Memory (RAM), cache memory (cache), and/or the like. The non-volatile memory may include, for example, Read Only Memory (ROM), hard disk, flash memory, etc.

The processor of the display unit 902 may be a Central Processing Unit (CPU), image processing unit (GPU), Application Specific Integrated Circuit (ASIC), Field Programmable Gate Array (FPGA) or other form of processing unit having data processing capability and/or instruction execution capability, and may control other components in the ranging apparatus to perform desired functions. The processor of the display unit 902 can execute the instructions stored in the memory to perform the method for noise filtering of the point cloud of the ranging apparatus described herein, which is described in the foregoing embodiments and is not repeated herein. For example, a processor can include one or more embedded processors, processor cores, microprocessors, logic circuits, hardware Finite State Machines (FSMs), Digital Signal Processors (DSPs), or a combination thereof. In this embodiment, the processor comprises a Field Programmable Gate Array (FPGA).

The input device may be a device used by a user to input an instruction (e.g., an instruction for inputting a user to display the point cloud data after noise is filtered or to display the point cloud data containing noise), and may include one or more of a keyboard, a mouse, a microphone, a touch screen, and the like.

The display of the display unit may be used to display the point cloud data after noise points are filtered out or to display the point cloud data containing noise points.

The display unit 902 further comprises a communication interface (not shown) for communication connection with the distance measuring device 901 to obtain the point cloud data output by the distance measuring device, which comprises wired or wireless communication. The display unit may access a wireless network based on a communication standard, such as WiFi, 2G, 3G, 4G, 5G, or a combination thereof. In one exemplary embodiment, the communication interface receives a broadcast signal or broadcast associated information from an external broadcast management system via a broadcast channel. In one exemplary embodiment, the communication interface further comprises a Near Field Communication (NFC) module to facilitate short-range communication. For example, the NFC module may 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 one example, the display unit further comprises a controller for controlling whether the distance measuring device executes the method according to an instruction input by a user, when the user needs to execute the method of point cloud noise filtering, a first instruction is input, and when the user does not need to execute the method of point cloud noise filtering, a second instruction is input, and the distance measuring device executes a corresponding action according to the received instruction, so that whether the distance measuring device executes the method of point cloud noise filtering is controlled according to the user requirement, and the flexibility of the distance measuring system is improved.

In one example, the display unit 902 is further configured to: acquiring point cloud data output by the distance measuring device, wherein the point cloud data can be original sampling data collected by the distance measuring device and can comprise related data of each characteristic information described in the foregoing; determining whether to execute the method for filtering the point cloud described above according to an instruction input by a user to filter noise in the point cloud data, that is, determining whether to execute the method for filtering the point cloud described above by a display unit according to the instruction input by the user, and displaying the point cloud data after the noise is filtered or displaying the point cloud data containing the noise in real time. The display unit is arranged to enable a user to view point cloud data more visually, and the display unit can also determine whether to execute a point cloud noise filtering method according to the user requirement by acquiring original sampling data of the distance measuring device, so that the point cloud noise filtering method can be executed to filter the point cloud data when the user needs the method, and only the point cloud data is displayed when the user does not need the method, therefore, the flexibility is higher, and the user experience is better.

The distance measuring system of the embodiment of the invention can also be used for realizing the point cloud noise filtering method described above, so that the distance measuring system also has the advantages of the point cloud noise filtering method described above.

In summary, the point cloud noise filtering method, the distance measuring device and the distance measuring system of the embodiment of the invention have the following advantages: 1) the method breaks through the limitation of single pulse sampling, makes full use of the inherent time sequence information and original sampling data of the detection point, and can effectively filter light noise, rain fog noise, crosstalk noise, wire drawing noise and the like which cannot be identified by the existing scheme. 2) The point cloud noise filtering method can be realized in bottom firmware of the distance measuring device, can more accurately distinguish noise points and non-noise points by utilizing original sampling data, and the probability of misjudgment and missed judgment in actual use is far less than that of the existing noise filtering scheme based on the upper algorithm of the space coordinate relationship. (3) The method for filtering the noise of the point cloud realizes 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 comprises a data buffer area for realizing the storage and calculation of data.

In an embodiment, the distance measuring device according to the embodiments of the present invention may be applied to a mobile platform, and the distance measuring device and/or the distance measuring system may be mounted on a platform body of the mobile platform. The mobile platform with the distance measuring device can measure the external environment, for example, the distance between the mobile platform and an obstacle is measured for the purpose of avoiding the obstacle, and the external environment is mapped in two dimensions or three dimensions. In certain embodiments, the mobile platform comprises at least one of an unmanned aerial vehicle, an automobile, a remote control car, a robot, a boat, a camera. When the distance measuring device is applied to the unmanned aerial vehicle, the platform body is a fuselage of the unmanned aerial vehicle. When the distance measuring device is applied to an automobile, the platform body is the automobile body of the automobile. The vehicle may be an autonomous vehicle or a semi-autonomous vehicle, without limitation. When the distance measuring device is applied to the remote control car, the platform body is the car body of the remote control car. When the distance measuring device is applied to a robot, the platform body is the robot. When the distance measuring device is applied to a camera, the platform body is the camera itself.

The distance measuring device in the embodiment of the invention is used for executing the method, and the mobile platform comprises the distance measuring device, so that the distance measuring device and the mobile platform have the same advantages as the method.

In addition, the embodiment of the invention also provides a computer storage medium, and the computer storage medium is stored with the computer program. One or more computer program instructions may be stored on the computer-readable storage medium, and a processor may execute the program instructions stored by the storage device to implement the functions (implemented by the processor) of the embodiments of the present invention described herein and/or other desired functions, such as to execute the corresponding steps of the method for point cloud noise filtering of a ranging apparatus according to the embodiments of the present invention, and various applications and various data, such as various data used and/or generated by the applications, and the like, may also be stored in the computer-readable storage medium.

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), a portable compact disc read only memory (CD-ROM), a 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 may contain 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 portions of the present application may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the various steps or methods may be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. If implemented in hardware, as in another embodiment, any one or combination of the following techniques, which are known in the art, may be used: a discrete logic circuit having a logic Gate circuit for implementing a logic function on a data signal, an asic having a suitable combinational logic Gate circuit, a Programmable Gate Array (PGA), a Field Programmable Gate Array (FPGA), and the like.

Although the illustrative embodiments have been described herein with reference to the accompanying drawings, it is to be understood that the foregoing illustrative embodiments are merely exemplary and are not intended to limit the scope of the invention thereto. Various changes and modifications may be effected therein by one of ordinary skill in the pertinent art without departing from the scope or spirit of the present invention. All such changes and modifications are intended to be included within the scope of the present invention as set forth in the appended claims.

Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.

In the several embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. For example, the above-described device embodiments are merely illustrative, and for example, the division of the units is only one logical functional division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another device, or some features may be omitted, or not executed.

In the description provided herein, numerous specific details are set forth. It is understood, however, that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, structures and techniques have not been shown in detail in order not to obscure an understanding of this description.

Similarly, it should be appreciated that in the description of exemplary embodiments of the invention, various features of the invention are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the invention and aiding in the understanding of one or more of the various inventive aspects. However, the method of the present invention should not be construed to reflect the intent: that the invention as claimed requires more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive aspects lie in less than all features of a single disclosed embodiment. Thus, the claims following the detailed description are hereby expressly incorporated into this detailed description, with each claim standing on its own as a separate embodiment of this invention.

It will be understood by those skilled in the art that all of the features disclosed in this specification (including any accompanying claims, abstract and drawings), and all of the processes or elements of any method or apparatus so disclosed, may be combined in any combination, except combinations where such features are mutually exclusive. Each feature disclosed in this specification (including any accompanying claims, abstract and drawings) may be replaced by alternative features serving the same, equivalent or similar purpose, unless expressly stated otherwise.

Furthermore, those skilled in the art will appreciate that while some embodiments described herein include some features included in other embodiments, rather than other features, combinations of features of different embodiments are meant to be within the scope of the invention and form different embodiments. For example, in the claims, any of the claimed embodiments may be used in any combination.

The various component embodiments of the invention may be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. Those skilled in the art will appreciate that a microprocessor or Digital Signal Processor (DSP) may be used in practice to implement some or all of the functionality of some of the modules according to embodiments of the present invention. The present invention may also be embodied as apparatus programs (e.g., computer programs and computer program products) for performing a portion or all of the methods described herein. Such programs implementing the present invention may be stored on computer-readable media or may be in the form of one or more signals. Such a signal may be downloaded from an internet website or provided on a carrier signal or in any other form.

It should be noted that the above-mentioned embodiments illustrate rather than limit the invention, and that those skilled in the art will be able to design alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The invention may be implemented by means of hardware comprising several distinct elements, and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by one and the same item of hardware. The usage of the words first, second and third, etcetera do not indicate any ordering. These words may be interpreted as names.

Claims

A method for point cloud noise filtering of a distance measuring device, the method comprising:

acquiring characteristic information of each point cloud point in point cloud data acquired by the distance measuring device in a preset continuous time window, wherein the characteristic information comprises at least one of the following information: depth, scan angle, spatial curvature, characteristics of pulse waveform, reflectivity, echo energy;

and determining noise points in the point cloud data collected in a 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 or not, wherein the noise points comprise at least one point cloud point which is discontinuous with the characteristic information of other point cloud points in the preset continuous time window.
The method of claim 1, wherein determining noise in the point cloud data acquired within a preset continuous time window according to whether a change in the characteristic information of successive point cloud points within the preset continuous time window is continuous comprises:

determining the isolation degree of the characteristic information of the current point cloud point, wherein the isolation degree is used for determining whether the change of the characteristic information of the current point cloud point is continuous with at least one point cloud point in a preorder time window before the current point cloud point and/or at least one point cloud point in a subsequent time window;

and determining whether the cloud point of the current point is a noise point according to the isolation degree of the characteristic information of the cloud point of the current point.
The method of claim 2, wherein determining whether a current point cloud point is noisy based on an isolation of the feature information for the current point cloud point comprises:

determining the isolation degree of the characteristic information of the cloud point of the current point based on a preset mapping function;

and determining whether the current point cloud point is a noise point according to a comparison result of the isolation degree of the characteristic information and a set threshold, wherein when the isolation degree of the characteristic information is greater than the set threshold, the current point cloud point is determined to be the noise point.
The method of claim 3, wherein the feature information comprises a depth, the predetermined mapping function is determined based on a difference between depth values of the current point cloud point and at least one neighboring point cloud point, the neighboring point cloud point being a point cloud point acquired before the current point cloud point.
The method of claim 4, wherein the set threshold is determined based on a scanning angular velocity of the ranging device, a maximum filtering angle, and a depth value of the current point cloud point, wherein the maximum filtering angle is an angle between an advancing direction of a light pulse train emitted by the ranging device and a reference plane.
The method of claim 1, wherein the characteristic information comprises a scan angle, wherein the scan angle of a current point cloud point is an angle between a space vector of the current point cloud point pointing to an adjacent point cloud point adjacent to the current point cloud point and a space vector of the current point cloud point, and wherein the adjacent point cloud point is a point cloud point acquired before the current point cloud point.
The method of claim 6, wherein determining noise in the point cloud data collected within a preset continuous time window according to whether a change in the characteristic information of successive point cloud points within the preset continuous time window is continuous comprises:

the method comprises the steps of obtaining a scanning angle of each point cloud point in a preset continuous time window, wherein the scanning angle of the point cloud point collected before a first time point in the preset continuous time window is above a first threshold scanning angle, and the point cloud point with the scanning angle smaller than the first threshold scanning angle and smaller than a set threshold of the scanning angle is a noise point.
The method of claim 6, wherein determining noise in the point cloud data collected within a preset continuous time window according to whether a change in the characteristic information of successive point cloud points within the preset continuous time window is continuous comprises:

taking a minimum scanning angle value from a scanning angle of a point cloud point collected in a preamble time window before a current point cloud point, a scanning angle of a point cloud point collected in a subsequent time window after the current point cloud point and a scanning angle of the current point cloud point;

and determining whether the point cloud point corresponding to the minimum scanning angle value is a noise point according to the comparison result of the minimum scanning angle value and the set threshold of the scanning angle, wherein when the minimum scanning angle value is smaller than the set threshold of the scanning angle, the point cloud point corresponding to the minimum scanning angle value is determined to be the noise point.
The method of claim 8, wherein when the preamble time window is 0 and a subsequent time window is 1, the minimum scan angle value is a minimum value of a scan angle of the current point cloud point and a scan angle of a neighboring point cloud point adjacent to and after the current point cloud point;

or, the preamble time window is 1, the subsequent time window is 0, and the minimum scan angle value is a minimum value of a scan angle of the current cloud point and a scan angle of an adjacent cloud point adjacent to the current cloud point in front of the current cloud point.
The method of claim 6, wherein determining noise in the point cloud data collected within a preset continuous time window according to whether a change in the characteristic information of successive point cloud points within the preset continuous time window is continuous comprises:

acquiring a scanning angle of each point cloud point in a preset continuous time window containing the acquisition time of the current point cloud point;

determining the maximum number of point cloud points of which the scanning angles acquired in the preset continuous time window are smaller than a set threshold of the scanning angles;

and determining whether the current point cloud points are noise points according to the comparison result of the maximum number and the preset number, wherein when the maximum number is smaller than the preset number, the current point cloud points are determined to be noise points.
The method of claim 3, wherein the feature information comprises spatial curvature, and determining whether the current point cloud point is noisy based on the comparison of the isolation with a set threshold comprises:

and determining whether the current point cloud point is a noise point according to a comparison result of the isolation degree of the space curvature of the current point cloud point and a set threshold, wherein when the isolation degree of the space curvature of the current point cloud point is greater than the set threshold, the current point cloud point is determined to be the noise point.
The method of claim 1, wherein the characteristic of the pulse shape comprises at least one of a pulse width, a pulse height, and a pulse area of the pulse shape.
The method of claim 1, wherein the characteristic of the pulse waveform comprises a pulse width of the pulse waveform, the pulse width being a difference between a cut-off time and an arrival time, wherein the arrival time is a time when the echo signal first triggers a time-to-digital converter minimum threshold, and the cut-off time is a time when the echo signal second triggers a time-to-digital converter minimum threshold.
The method of claim 13, wherein determining noise in the point cloud data collected within a preset continuous time window according to whether a change in the characteristic information of successive point cloud points within the preset continuous time window is continuous comprises:

determining the abnormal spread width of the point cloud points collected in the preset continuous time window according to a preset mapping function;

and determining noise points in the point cloud data collected in the preset continuous time window according to the abnormal broadening widths, wherein the noise points comprise point cloud points with the abnormal broadening degrees larger than a set threshold value.
The method of claim 14, wherein a preset mapping function of a current point cloud point is set according to whether the pulse width, arrival time, and cutoff time of consecutive point cloud points of the preset continuous time window satisfy a preset filtering condition, wherein when the preset filtering condition is satisfied, an 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 filtering condition is not satisfied, the abnormal spread 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 the current cloud point is determined to be noise when the pulse width, arrival time, and cutoff time satisfy the preset filtering condition, wherein the preset filtering condition comprises at least one of:

the method comprises the steps that the pulse width of an echo signal at a first time point in a preamble time window of a cloud point at a current point is increased compared with the pulse width at the previous moment of the first time point, wherein the increased pulse width is larger than a first pulse width setting threshold;

the pulse width of an echo signal at a second time point in a subsequent time window of a cloud point of a current point is suddenly reduced compared with the pulse width at the previous moment of the second time point, wherein the sudden reduction is larger than a first pulse width setting threshold value;

an arrival time within a time window between the first time point and the second time point is within a first threshold from a preamble arrival time of the first time point and a cutoff time within the time window is within a second threshold from a subsequent cutoff time of the second time point, or the cutoff time within the time window between the first time point and the second time point is within a third threshold time from the preamble cutoff time of the first time point and the arrival time within the time window is within a 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 of claim 1, wherein the characteristic information comprises reflectivity, and determining noise in the point cloud data collected within a preset continuous time window according to whether a change in the characteristic information of successive point cloud points within the preset continuous time window is continuous comprises:

and the reflectivity of the continuous point cloud points in any time window in the preset continuous time window is reduced from the first reflectivity to the second reflectivity, and the reflectivity of the point cloud points is increased from the second reflectivity to the third reflectivity according to the time sequence, 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.
The method of claim 17, wherein the characteristic information comprises reflectivity, and determining noise in the point cloud data collected within a preset continuous time window according to whether a change in the characteristic information of successive point cloud points within the preset continuous time window is continuous comprises:

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

and determining noise points in the point cloud data collected in the preset continuous time window according to the reflectivity mutation degree, wherein the noise points comprise point cloud points with the reflectivity mutation degree larger than a set threshold value.
The method of claim 18, wherein the preset mapping function of the current point cloud point is set according to whether the reflectivity of the consecutive point cloud points of the preset consecutive time window satisfies a preset filtering condition, wherein when the preset filtering condition is satisfied, the reflectivity jump degree of the current point cloud point determined according to the preset mapping function is greater than a set threshold, and when the preset filtering condition is not satisfied, the reflectivity jump degree of the current point cloud point determined according to the preset mapping function is less than the set threshold.
The method of claim 19, wherein the preset continuous time window comprises a preceding time window and a following time window, wherein the preceding time window is located before the acquisition time of the current point cloud point, the following time window is located after the acquisition time of the current point cloud point, and the preset filtering condition comprises the following condition:

the reflectivity of the point cloud point collected at the first time point of the preamble time window is reduced to be lower than a first threshold reflectivity, and the difference value between the reflectivity of the point cloud point collected before the first time point and the reflectivity of the point cloud point collected at the first time point is larger than a set threshold;

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

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

the acquisition time of the cloud point at the current point is between the first time point and the second time point.
The method of any one of claims 1 to 20, wherein one of the point cloud points is identified as noisy before another point cloud point is acquired in the point cloud data.
The method of any one of claims 1 to 21, wherein the method is implemented in a field programmable gate array, FPGA, in the ranging apparatus.
The method of any one of claims 1 to 22, wherein the embedded underlying firmware in the ranging device comprises a data buffer for storing characteristic information of point cloud points acquired within a continuous time window;

the determining noise points 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 or not comprises the following steps:

and extracting the characteristic information of the point cloud points collected in the continuous time window from the data buffer area, and determining the noise points in the point cloud data collected in the preset continuous time window.
The method of any one of claims 1 to 22, wherein the method is implemented in a display unit, wherein the display unit is communicatively connected to the ranging device.
A ranging device comprising one or more processors, working together or separately, the processors being configured to:

acquiring characteristic information of each point cloud point in point cloud data acquired by the distance measuring device in a preset continuous time window, wherein the characteristic information comprises at least one of the following information: depth, scan angle, spatial curvature, characteristics of pulse waveform, reflectivity, echo energy;

and determining noise points in the point cloud data collected in a 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 or not, wherein the noise points comprise at least one point cloud point which is discontinuous with the characteristic information of other point cloud points in the preset continuous time window.
The ranging apparatus of claim 25, wherein the processor is further configured to:

determining the isolation degree of the characteristic information of the current point cloud point, wherein the isolation degree is used for determining whether the change of the characteristic information of the current point cloud point is continuous with at least one point cloud point in a preorder time window before the current point cloud point and/or at least one point cloud point in a subsequent time window;

and determining whether the cloud point of the current point is a noise point according to the isolation degree of the characteristic information of the cloud point of the current point.
The ranging apparatus of claim 26, wherein the processor is further configured to:

determining the isolation degree of the characteristic information of the cloud point of the current point based on a preset mapping function;

and determining whether the current point cloud point is a noise point according to a comparison result of the isolation degree of the characteristic information and a set threshold, wherein when the isolation degree of the characteristic information is greater than the set threshold, the current point cloud point is determined to be the noise point.
The ranging apparatus of claim 27, wherein the feature information comprises a depth, the preset mapping function is determined based on a difference between depth values of the current point cloud point and at least one neighboring point cloud point, the neighboring point cloud point being a point cloud point acquired before the current point cloud point.
The ranging apparatus of claim 27, wherein the set threshold is determined based on a scanning angular velocity of the ranging apparatus, a maximum filtering angle, and a depth value of the current cloud point, wherein the maximum filtering angle is an angle between a forward direction of a light pulse train emitted by the ranging apparatus and a reference plane.
The range finding apparatus of claim 25, wherein the characteristic information comprises a scan angle, wherein the scan angle of the current point cloud point is an angle between a space vector of the current point cloud point pointing to an adjacent point cloud point adjacent to the current point cloud point and a space vector of the current point cloud point, and wherein the adjacent point cloud point is a point cloud point collected before the current point cloud point.
The ranging apparatus of claim 30, wherein the processor is further configured to:

the method comprises the steps of obtaining a scanning angle of each point cloud point in a preset continuous time window, wherein the scanning angle of the point cloud point collected before a first time point in the preset continuous time window is above a first threshold scanning angle, and the point cloud point with the scanning angle smaller than the first threshold scanning angle and smaller than a set threshold of the scanning angle is a noise point.
The ranging apparatus of claim 30, wherein the processor is further configured to:

taking a minimum scanning angle value from a scanning angle of a point cloud point collected in a preamble time window before a current point cloud point, a scanning angle of a point cloud point collected in a subsequent time window after the current point cloud point and a scanning angle of the current point cloud point;

and determining whether the point cloud point corresponding to the minimum scanning angle value is a noise point according to the comparison result of the minimum scanning angle value and the set threshold of the scanning angle, wherein when the minimum scanning angle value is smaller than the set threshold of the scanning angle, the point cloud point corresponding to the minimum scanning angle value is determined to be the noise point.
The ranging apparatus of claim 32, wherein when the preamble time window is 0 and the subsequent time window is 1, the minimum scan angle value is a minimum value of a scan angle of the current cloud point and a scan angle of a neighboring cloud point adjacent to the current cloud point;

or, the preamble time window is 1, the subsequent time window is 0, and the minimum scan angle value is a minimum value of a scan angle of the current cloud point and a scan angle of an adjacent cloud point adjacent to the current cloud point in front of the current cloud point.
The ranging apparatus of claim 30, wherein the processor is further configured to:

acquiring a scanning angle of each cloud point in a preset continuous time window containing the acquisition time of the current cloud point;

determining the maximum number of the point cloud points of which the collected scanning angles are smaller than a set threshold value of the scanning angles in the preset continuous time window;

and determining whether the current point cloud points are noise points according to the comparison result of the maximum number and the preset number, wherein when the maximum number is smaller than the preset number, the current point cloud points are determined to be noise points.
The range finder device of claim 27, wherein the characteristic information includes spatial curvature, and determining whether the current point cloud point is noise based on the comparison of the isolation with a set threshold comprises:

and determining whether the current point cloud point is a noise point according to a comparison result of the isolation degree of the space curvature of the current point cloud point and a set threshold, wherein when the isolation degree of the space curvature of the current point cloud point is greater than the set threshold, the current point cloud point is determined to be the noise point.
The ranging apparatus of claim 25, wherein the characteristic of the pulse waveform comprises at least one of a pulse width, a pulse height, and a pulse area of the pulse waveform.
The ranging apparatus of claim 25 wherein the characteristic of the pulse waveform comprises a pulse width of the pulse waveform, the pulse width being a difference between a time-off and a time-of-arrival, wherein the time-of-arrival is a time at which the echo signal first triggers a time-to-digital converter minimum threshold and the time-off is a time at which the echo signal second triggers a time-to-digital converter minimum threshold.
The ranging apparatus of claim 37, wherein the processor is further configured to:

determining the abnormal spread width of the point cloud points collected in the preset continuous time window according to a preset mapping function;

and determining noise points in the point cloud data collected in the preset continuous time window according to the abnormal broadening widths, wherein the noise points comprise point cloud points with the abnormal broadening degrees larger than a set threshold value.
The range finding device of claim 38, wherein the preset mapping function of the current point cloud point is set according to whether the pulse width, the arrival time and the cutoff time of the continuous point cloud points of the preset continuous time window satisfy a preset filtering condition, wherein when the preset filtering condition is satisfied, 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 filtering condition is not satisfied, the abnormal spread of the current point cloud point determined according to the preset mapping function is less than a set threshold.
The range finder device of claim 39, wherein the current point cloud is determined to be noisy when the pulse width, arrival time, and cutoff time satisfy the preset filtering condition, the preset filtering condition comprising at least one of:

the method comprises the steps that the pulse width of an echo signal at a first time point in a preamble time window of a cloud point at a current point is increased compared with the pulse width at the previous moment of the first time point, wherein the increased pulse width is larger than a first pulse width setting threshold;

the pulse width of an echo signal at a second time point in a subsequent time window of a cloud point of a current point is suddenly reduced compared with the pulse width at the previous moment of the second time point, wherein the sudden reduction is larger than a first pulse width setting threshold value;

an arrival time within a time window between the first time point and the second time point is within a first threshold from a preamble arrival time of the first time point and a cutoff time within the time window is within a second threshold from a subsequent cutoff time of the second time point, or the cutoff time within the time window between the first time point and the second time point is within a third threshold time from the preamble cutoff time of the first time point and the arrival time within the time window is within a 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 ranging apparatus of claim 25, wherein the characterization information comprises reflectivity, the processor further configured to:

and the reflectivity of the continuous point cloud points in any time window in the preset continuous time window is reduced from the first reflectivity to the second reflectivity, and the reflectivity of the point cloud points is increased from the second reflectivity to the third reflectivity according to the time sequence, 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.
The ranging apparatus of claim 41 wherein the characterization information comprises reflectivity, the processor further configured to:

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

and determining noise points in the point cloud data collected in the preset continuous time window according to the reflectivity mutation degree, wherein the noise points comprise point cloud points with the reflectivity mutation degree larger than a set threshold value.
The range finding device of claim 42, wherein the preset mapping function of the current point cloud point is set according to whether the reflectivity of the consecutive point cloud points in the preset consecutive time window satisfies a preset filtering condition, wherein when the preset filtering condition is satisfied, the reflectivity jump degree of the current point cloud point determined according to the preset mapping function is greater than a set threshold, and when the preset filtering condition is not satisfied, the reflectivity jump degree of the current point cloud point determined according to the preset mapping function is less than a set threshold.
The ranging apparatus of claim 43, wherein the preset continuous time window comprises a preamble time window and a subsequent time window, wherein the preamble time window is located before the acquisition time of the current cloud point, wherein the subsequent time window is located after the acquisition time of the current cloud point, and wherein the preset filtering condition comprises the following condition:

the reflectivity of the point cloud point collected at the first time point of the preamble time window is reduced to be lower than a first threshold reflectivity, and the difference value between the reflectivity of the point cloud point collected before the first time point and the reflectivity of the point cloud point collected at the first time point is larger than a set threshold;

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

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

the acquisition time of the cloud point at the current point is between the first time point and the second time point.
A ranging apparatus as claimed in any of claims 25 to 44 wherein one of the point cloud points is identified as noisy before another point cloud point is acquired in the point cloud data.
A ranging apparatus as claimed in any of claims 25 to 45 wherein the processor comprises a field programmable gate array FPGA.
A ranging device as claimed in any of claims 25 to 45 wherein the embedded underlying firmware in the ranging device comprises a data buffer for storing characteristic information of point cloud points acquired within a continuous time window;

the determining noise points 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 or not comprises the following steps:

and extracting the characteristic information of the point cloud points collected in the continuous time window from the data buffer area, and determining the noise points in the point cloud data collected in the preset continuous time window.
A ranging system comprising a ranging device and a display unit, wherein one of the ranging device and the display unit is adapted to implement the method of any of claims 1 to 24.
The ranging system of claim 48 wherein the ranging apparatus comprises a memory and a processor to store executable instructions, the processor to execute the instructions stored in the memory to cause the processor to perform the method.
The range finding system of claim 49, wherein the display unit is configured to:

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

and displaying the point cloud data after the noise points are filtered or displaying the point cloud data containing the noise points according to an instruction input by a user.
The range finding system of claim 48, wherein the display unit is further configured to: and controlling whether the distance measuring device executes the method or not according to an instruction input by a user.
The range finding system of claim 48, wherein the display unit is further configured to:

acquiring point cloud data output by the distance measuring device;

determining, according to an instruction input by a user, whether to perform the method of any one of claims 1 to 21 to filter noise in the point cloud data;

and

and displaying the point cloud data after the noise points are filtered in real time or displaying the point cloud data containing the noise points.
A computer storage medium on which a computer program is stored, which program, when executed by a processor, carries out the method of any one of claims 1 to 24.
A mobile platform, comprising:

a ranging apparatus as claimed in any of claims 25 to 47 or a ranging system as claimed in any of claims 48 to 52; and

the platform body, range unit installs on the platform body.
The mobile platform of claim 54, wherein the mobile platform comprises a drone, a robot, a vehicle, or a boat.