WO2021056348A1

WO2021056348A1 - Signal processing method of point cloud detection system, and point cloud detection system

Info

Publication number: WO2021056348A1
Application number: PCT/CN2019/108237
Authority: WO
Inventors: 许友; 陈涵
Original assignee: 深圳市大疆创新科技有限公司
Priority date: 2019-09-26
Filing date: 2019-09-26
Publication date: 2021-04-01
Also published as: CN115643809A

Abstract

The present application provides a signal processing method of a point cloud detection system, and a point cloud detection system. The method comprises: emitting an optical pulse signal, and receiving an echo signal corresponding to the optical pulse signal; obtaining point cloud data on the basis of the echo signal, and analyzing the point cloud data to determine at least one of the following attributes of the point cloud data: the number of recorded echoes, the shape of the recorded echo, the type of a detected point, a noise type, and a noise confidence; and outputting point cloud data comprising a result of the analysis. According to the signal processing method of a point cloud detection system and the point cloud detection system in embodiments of the present application, attribute information of point cloud data is analyzed, and point cloud data comprising the attribute information is output. Therefore, information obtained by the point cloud detection system can be retained to the greatest extent, blank output information caused by false filtering of noise at the bottom layer is avoided, and the processing effect of an upper layer algorithm is improved.

Description

Signal processing method of point cloud detection system and point cloud detection system

Manual

Technical field

The present invention generally relates to the technical field of point cloud detection, and more specifically to a signal processing method of a point cloud detection system and a point cloud detection system.

Background technique

At present, after the point cloud detection system quickly scans the scene in the field of view (Field of View, FOV), the output point cloud information of the measured scene in the FOV usually includes the three-dimensional space coordinates (polar coordinates or rectangular coordinates) of the detected object. ), reflectance (or intensity), and time stamp information of the scan. The point cloud detection system processes the photoelectric signals through the underlying algorithm to obtain the above-mentioned point cloud information, and transmits the point cloud information to the upper algorithm for post-processing of the point cloud data, so as to be used in surveying and mapping, high-precision maps, automatic driving and other fields. However, because the existing point cloud detection system only outputs the above-mentioned information, the input information of the upper-layer algorithm is relatively limited, which may affect the reliability and accuracy of the point cloud data post-processing.

Summary of the invention

The present invention is proposed in order to solve at least one of the above-mentioned problems. The present invention provides a signal processing scheme for a point cloud detection system, which analyzes the attribute information of the point cloud data and outputs point cloud data including the attribute information, which can retain the information obtained by the point cloud detection system to the greatest extent and eliminate The output information is blank due to the false filtering of noise at the bottom layer, which is beneficial to improve the processing effect of the upper layer algorithms (such as filtering, recognition, and segmentation algorithms). The following briefly describes the video encoding and decoding solution proposed by the present invention, and more details will be described in the specific implementation in conjunction with the accompanying drawings.

According to one aspect of the present invention, there is provided a signal processing method of a point cloud detection system. The method includes: transmitting an optical pulse signal, and receiving an echo signal corresponding to the optical pulse signal; and obtaining a point based on the echo signal. Cloud data, and analyze the point cloud data to determine at least one of the following attributes of the point cloud data: the number of recorded echoes, the shape of the recorded echoes, the type of the measured point to which it belongs, the type of noise it belongs to, Noise confidence level; and output point cloud data including the result of the analysis.

In an embodiment of the present invention, each item of attribute information in the analysis result is used as one item of tag information of the point cloud data.

In an embodiment of the present invention, all item attribute information in the analysis result is used as one item of tag information of the point cloud data.

In an embodiment of the present invention, the format of the tag information is configured based on the principle of minimizing storage.

In an embodiment of the present invention, the method further includes: calculating and outputting position information, reflectivity information, and time stamp information of the point cloud data.

In an embodiment of the present invention, the output includes the point cloud data of the analysis result, including:

Write the point cloud data including the analysis result into the data field of a data frame, and output the data frame. The data frame includes a position information field, a reflectance information field, a time stamp information field, and a tag Information field.

In an embodiment of the present invention, the tag information field includes 1 byte of data.

In an embodiment of the present invention, each two bits of the tag information field form a group of data from low to high, and each group of data represents one item of attribute information in the tag information.

In an embodiment of the present invention, the first set of data in the tag information field is used to represent point attributes based on spatial location, the second set of data is used to represent point attributes based on echo intensity, and the third set of data is used to Represents the echo sequence number.

In an embodiment of the present invention, the spatial location-based point attributes include: normal points, first-level noise, second-level noise, and third-level noise. The higher the noise level, the stronger the noise filtering strength.

In an embodiment of the present invention, the echo intensity-based point attributes include: normal points, first-level noise points, and second-level noise points. The larger the noise point level, the stronger the echo energy and the stronger the noise filtering intensity.

In an embodiment of the present invention, the type of noise includes at least one of the following: rain and fog noise, light noise, electrical noise, crosstalk noise, and dust noise.

In an embodiment of the present invention, the type of the measured point is at least one of the following types: including sky point, ground point, vegetation point, water area point, and road sign point.

In an embodiment of the present invention, the types of measured points include dynamic points and static points.

According to another aspect of the present invention, a point cloud detection system is provided. The system includes: a transmitting end device for transmitting an optical pulse signal; a receiving end device for receiving an echo signal corresponding to the optical pulse signal; And a processor, configured to obtain point cloud data based on the echo signal, and analyze the point cloud data to determine at least one of the following attributes of the point cloud data: the number of recorded echoes, the number of recorded echoes The shape, the type of the measured point to which it belongs, the type of the noise to which it belongs, the confidence of the noise, and the output of the point cloud data including the result of the analysis.

In an embodiment of the present invention, the processor is further used to calculate and output the position information, reflectivity information, and time stamp information of the point cloud data.

In an embodiment of the present invention, the processor is further configured to: write the point cloud data including the analysis result into the data field of a data frame respectively, and output the data frame, the data The frame includes a location information field, a reflectance information field, a time stamp information field, and a tag information field.

In an embodiment of the present invention, the echo intensity-based point attributes include normal points, first-level noise points, and second-level noise points. The higher the noise point level, the stronger the echo energy and the stronger the noise filtering strength.

In an embodiment of the present invention, the measured point type includes at least one of the following: sky point, ground point, vegetation point, water area point, and road sign point.

The signal processing method of the point cloud detection system and the point cloud detection system according to the embodiments of the present invention analyze the attribute information of the point cloud data and output the point cloud data including the attribute information, which can retain the points acquired by the point cloud detection system to the greatest extent Information, eliminates the output information blank due to the false filtering of noise at the bottom layer, which is conducive to improving the processing effect of the upper layer algorithms (such as filtering, recognition, and segmentation algorithms).

Description of the drawings

Figure 1 shows a schematic structural diagram of a point cloud detection system that can implement the solution of the present invention.

Fig. 2 shows a schematic flowchart of a signal processing method of a point cloud detection system according to an embodiment of the present invention.

Fig. 3 shows an exemplary schematic diagram of an application scenario of a signal processing method of a point cloud detection system according to an embodiment of the present invention.

Fig. 4 shows a schematic block diagram of a point cloud detection system according to an embodiment of the present invention.

detailed description

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

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

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

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

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

Figure 1 shows a schematic structural diagram of a point cloud detection system that can implement the solution of the present invention. As shown in Figure 1, the point cloud detection system may include a laser 101, a lens 102, a controller 103, a first motor 104, a second motor 105, a first prism 106, a second prism 107, a beam splitter 108, and echo A receiver 109 and a time of flight (TOF) module 110, wherein the echo receiver 109 includes a photodiode, for example, it may be an avalanche photodiode (APD). Taking the distance between the point cloud detection system and the target 20 as an example, the laser 101 of the point cloud detection system turns the electrical pulse signal into a divergent light pulse signal, and the lens 102 turns the divergent light pulse signal into a parallel light pulse signal and emits it. , The controller 103 (set in the chip) respectively controls the rotation of the first prism 106 through the first motor 104, and controls the rotation of the second prism 107 through the second motor 105, using the differential rotation of the first prism 106 and the second prism 107, Change the direction of the light pulse signal emitted after passing through the first prism 106 and the second prism 107. After the emitted light pulse signal meets the target 20, it will be reflected back to the light pulse signal, and the reflected light pulse signal will pass through the beam splitter 108 Split the beam and enter the echo receiver 109 (including APD). The echo receiver 109 converts the optical pulse signal into an electrical pulse signal, and calculates the point cloud detection system and the target through the TOF (set in the chip) module 110 The distance between 20 and the point cloud data is generated according to the distance between the point cloud detection system and the target 20.

In the embodiment of the present application, the point cloud detection system may be a laser radar, which can be applied to mobile platforms such as unmanned aerial vehicles, automobiles, remote control vehicles, robots, cameras, and the like.

FIG. 2 shows a schematic flowchart of a signal processing method 200 of a point cloud detection system according to an embodiment of the present application. As shown in FIG. 2, the method 200 includes the following steps:

In step S210, an optical pulse signal is transmitted, and an echo signal corresponding to the optical pulse signal is received.

In step S220, point cloud data is obtained based on the echo signal, and the point cloud data is analyzed to determine at least one of the following attributes of the point cloud data: the number of recorded echoes, the shape of the recorded echo, The type of the measured point to which it belongs, the type of noise it belongs to, and the confidence of the noise point.

In step S230, point cloud data including the result of the analysis is output.

In the embodiment of the present invention, the point cloud detection system transmits one optical pulse signal, and can receive one echo signal or multiple echo signals. Fig. 3 exemplarily shows a schematic diagram showing that the point cloud detection system transmits one optical pulse signal and receives two echo signals. As shown in Figure 3, when the point cloud detection system samples a certain time, the light pulse signal sent out hits the edge of object 1, and there is object 2 behind object 1, two echo signals will be generated, which are the first echo signal. (Ie the echo signal returned by the object 1), the second echo signal (ie the echo signal returned by the object 2). The first echo signal and the second echo signal are detected by the same optical pulse signal, so the directions of the first echo signal and the second echo signal are the same, but only the distance is inconsistent.

In the embodiment of the present invention, based on the echo signal received in step S210, the distance data corresponding to the echo signal can be determined, that is, the distance between the object corresponding to the echo signal and the point cloud detection system. If the point cloud detection system emits an optical pulse signal and receives an echo signal, the emission angle of the optical pulse signal and the distance data corresponding to the echo signal can be output as one point cloud data. In this case, the number of echoes recorded in the point cloud data is one. If the point cloud detection system emits one optical pulse signal and receives multiple echo signals, the emission angle of the one optical pulse signal and the distance data corresponding to each echo signal can be output as one point cloud data. In this case, the number of echoes recorded in the point cloud data is greater than one. In the embodiment of the present invention, the point cloud data can be analyzed to determine the number of echoes recorded by the point cloud data, and the number of echoes recorded by the point cloud data can be regarded as an attribute of the point cloud data. Information, the attribute information can be output along with the point cloud data, for example, transmitted to the upper-layer algorithm, so that the upper-layer algorithm can obtain more input information for further analysis by the upper-layer algorithm.

In the embodiment of the present invention, the point cloud data can be analyzed to determine the shape of the echo recorded by the point cloud data. The shape information of the echo refers to the waveform broadening and dragging of the received pulse due to factors such as the optical path medium and the emission angle. Distortion of the tail. The shape information of the echo may include, but is not limited to, pulse broadening, pulse fusion and other information. Similar to the foregoing, in the embodiment of the present invention, the shape of the echo recorded by the point cloud data can be used as an attribute information of the point cloud data, and the attribute information can be output with the point cloud data, for example, transmitted to the upper layer The algorithm allows the upper-level algorithm to obtain more input information for further analysis by the upper-level algorithm.

In the embodiment of the present invention, the point cloud data can be analyzed to determine the type of the measured point to which the point cloud data belongs. The type of measured point can indicate the specific category of the measured point, such as sky point, ground point, vegetation point, water area point, road sign point, etc.; or, the measured point can be distinguished from other dimensions, such as dynamic point, static point, etc. . Similar to the foregoing, in the embodiment of the present invention, the measured point type to which the point cloud data belongs can be used as an attribute information of the point cloud data, and the attribute information can be output along with the point cloud data, for example, transmitted to the upper algorithm , So that the upper-level algorithm can obtain more input information for further analysis by the upper-level algorithm.

In the embodiment of the present invention, the point cloud data can be analyzed to determine the type of noise to which the point cloud data belongs. Since there are inevitably various noise points in the point cloud data (such as photoelectric noise, crosstalk noise, dust noise, rain and fog noise, etc.), these noise points are mixed in the point cloud information, as the input of the upper layer algorithm, will cause the upper layer algorithm to misjudge ; In addition, if the noise is directly filtered in the underlying algorithm, there may be a lot of false filtering and missing filtering problems due to the limitations of the underlying algorithm. Based on this, if a point cloud data is likely to be noise after analysis, then in the embodiment of the present invention, the coordinate information of the point is not directly set to zero and the point is filtered out, but the specific type of the noise is further analyzed and identified , Such as rain and fog noise, light noise, electrical noise, crosstalk noise, dust noise, etc. Similar to the foregoing, in the embodiment of the present invention, the type of noise to which the point cloud data belongs can be used as a piece of attribute information of the point cloud data. The attribute information can be output with the point cloud data, for example, transmitted to the upper-layer algorithm to It is used for further analysis of the upper-level algorithm to avoid the problem of misfiltering due to analysis and recognition errors, and also to prevent the upper-level algorithm from losing other information (such as direction information, etc.) at this point.

In the embodiment of the present invention, the point cloud data can be analyzed to determine the noise confidence of the point cloud data. The noise confidence level can identify the probability that a point cloud point is a noise point. A value between 0 and 1 can be used to represent the noise point confidence level, or other different gear levels can be used to represent the noise point confidence level. Similar to the foregoing, in the embodiment of the present invention, the noise confidence of the point cloud data can be used as a piece of attribute information of the point cloud data, and the attribute information can be output with the point cloud data, for example, transmitted to the upper layer algorithm to It is used for further analysis of the upper-level algorithm, which can avoid the problems of leaky filtering and false filtering.

In the embodiment of the present invention, the point cloud data can be analyzed to determine part or all of the above-mentioned attribute information of the point cloud data, and part or all of the above-mentioned attribute information can be output together with the point cloud data, for example, transmitted to the upper layer The algorithm allows the upper-level algorithm to obtain more input information for further analysis by the upper-level algorithm. In other embodiments, the point cloud data can also be analyzed to determine other attribute information of the point cloud data, and other attribute information can also be output along with the point cloud data, for example, transmitted to the upper-level algorithm, so that the upper-level algorithm can obtain more information. The input information can be used in the upper algorithm for further analysis.

In an embodiment of the present invention, the various attribute information determined by analyzing the point cloud data can be used as a piece of tag information of the point cloud data. In another embodiment of the present invention, all item attribute information determined by analyzing the above-mentioned point cloud data can be used as one item of tag information of the point cloud data. The above-mentioned tag information can be written into a data field (such as a tag information field) of a data frame and output. Exemplarily, the format of the tag information may be configured based on the principle of minimizing storage.

The following description takes all item attribute information as one item of tag information of point cloud data as an example. The number of bits or bytes of the tag information field can be configured according to the attributes indicated by each attribute information included in the tag information. Taking the aforementioned attribute information as an example, assuming that the number of echoes recorded by the point cloud data is 4, two bits can be allocated to identify the number of echoes recorded by the point cloud data. Similarly, assuming that the type of noise to which the point cloud data belongs does not exceed 4, two bits can be allocated to identify the type of noise to which the point cloud data belongs. The number of bits or bytes allocated to the rest of the attribute information is also based on this principle, and no examples are given here.

In order to make the description clearer, the following describes an example of the tag information field with reference to Table 1 taking 1 byte as an example.

Table 1

As shown in Table 1, the tag information field of the point cloud data can be represented by 1 byte. The byte forms a set of data from low to high, and each set of data represents an attribute in the tag information. information. In the example shown in Table 1, bit0 and bit1 are the first group of data, bit2 and bit3 are the second group of data, bit4 and bit5 are the third group of data, and bit6 and bit7 are the fourth group of data.

Wherein, for example, the first set of data may be used to represent the point attribute based on the spatial position, that is, to determine whether the sampling point is a noise based on the spatial position of the sampling point. For example, when a point cloud detection system measures two objects that are very close before and after, a wire-like noise may be generated between the two objects. Exemplarily, such noise points can be divided into three levels, and the higher the level, the stronger the noise filtering strength. Exemplarily, 00 in the first set of data may indicate a normal point, 01 indicates a first-level noise, 10 indicates a second-level noise, and 11 indicates a third-level noise.

Continuing to refer to Table 1, the second set of data can be used to represent the point attributes based on intensity, that is, to determine whether the sampling point is a noise point based on the echo energy intensity. Under normal circumstances, the early echo energy of the laser beam is very small due to interference such as dust, rain, fog, and snow. Exemplarily, the noise can be divided into two levels according to the intensity of the echo energy. The larger the level, the stronger the echo energy, and the stronger the noise filtering. Exemplarily, 00 in the second set of data represents a normal point, 01 represents a first-level noise, such as dust points with weak echo energy, and 02 represents a second-level noise, such as rain and fog noise with slightly stronger echo energy.

Continuing to refer to Table 1, the third set of data can be used to indicate the echo sequence number, that is, the echo sequence of the sampling point. Since when using a coaxial optical path, even if there is no object to be measured outside, the internal optical system will generate an echo, so this echo can be recorded as the 0th echo. Subsequently, if there is a detectable object in the laser exit direction, the laser echo that first returns to the system is recorded as the first echo, followed by the second echo, and so on. Exemplarily, 00 in the third group of data represents the 0th echo, 01 represents the 1st echo, 10 represents the 2nd echo, and 11 represents the 3rd echo.

Continuing to refer to Table 1, in Table 1, the fourth group of data temporarily has no storage information, and is used as a reserved bit.

The above in conjunction with Table 1 exemplarily shows an example of the tag information field of the point cloud data in the method according to the embodiment of the present invention. It should be understood that this is only exemplary. In other examples, the tag information field of the point cloud data The size of, the number of data bits occupied by each attribute information, and the order of sequence, etc. can all be other situations.

In the embodiment of the present invention, the above method 200 may further include the following steps (not shown in FIG. 2): calculating and outputting the position information, reflectance information, and time stamp information of the point cloud data. Further, in combination with the previous description, the position information, reflectivity information, time stamp information, and tag information of the point cloud data can be written into the position information field, reflectivity information field, time stamp information field, and tag information of a data frame, respectively. Field, and output the data frame, for example, to an upper-layer algorithm. The upper-level algorithm can selectively use the above-mentioned information of the point cloud data according to the needs, which is beneficial to the compatibility and expansion of the upper-level algorithm.

Based on the above description, the signal processing method of the point cloud detection system according to the embodiment of the present invention analyzes the attribute information of the point cloud data and outputs the point cloud data including the attribute information, which can retain the points acquired by the point cloud detection system to the greatest extent Information, eliminates the output information blank due to the false filtering of noise at the bottom layer, which is conducive to improving the processing effect of the upper layer algorithms (such as filtering, recognition, and segmentation algorithms).

The following describes a point cloud detection system according to another aspect of the present invention with reference to FIG. 4. FIG. 4 shows a schematic block diagram of a point cloud detection system 400 according to an embodiment of the present invention. The point cloud detection system 400 includes a transmitting end device 410, a receiving end device 420, and a processor 430. Among them, the transmitting end device 410 is used to transmit optical pulse signals. The receiving end device 420 is configured to receive the echo signal corresponding to the optical pulse signal. The processor 430 is configured to obtain point cloud data based on the echo signal, and analyze the point cloud data to determine at least one of the following attributes of the point cloud data: the number of recorded echoes, and the shape of the recorded echoes , The type of the measured point to which it belongs, the type of the noise to which it belongs, and the confidence of the noise, and output the point cloud data including the analysis result.

In the embodiment of the present invention, the transmitting end device 410 of the point cloud detection system 400 transmits an optical pulse signal, and the receiving end device 420 may receive one echo signal or multiple echo signals. Based on the echo signal received by the receiving end device 420, the processor 430 may determine the distance data corresponding to the echo signal, that is, the distance between the object corresponding to the echo signal and the point cloud detection system. If the transmitting end device 410 of the point cloud detection system 400 transmits an optical pulse signal, and the receiving end device 420 receives an echo signal, the processor 430 can compare the transmission angle of the optical pulse signal with the echo signal. The distance data is output as a point cloud data. In this case, the number of echoes recorded in the point cloud data is 1. If the transmitting end device 410 of the point cloud detection system 400 transmits one optical pulse signal, and the receiving end device 420 receives multiple echo signals, the processor 430 may calculate the transmission angle of the one optical pulse signal and each echo signal. The corresponding distance data is output as a point cloud data. In this case, the number of echoes recorded in the point cloud data is greater than one. In the embodiment of the present invention, the processor 430 may analyze the point cloud data to determine the number of echoes recorded by the point cloud data, and use the number of echoes recorded by the point cloud data as the point cloud data An item of attribute information, which can be output along with the point cloud data, for example, transmitted to the upper-layer algorithm, so that the upper-layer algorithm can obtain more input information for further analysis by the upper-layer algorithm.

In the embodiment of the present invention, the processor 430 may analyze the point cloud data to determine the shape of the echo recorded by the point cloud data. The shape information of the echo refers to the waveform of the received pulse due to factors such as the optical path medium and the emission angle. Distortion such as widening and tailing. The shape information of the echo may include, but is not limited to, pulse broadening, pulse fusion and other information. Similar to the foregoing, in the embodiment of the present invention, the shape of the echo recorded by the point cloud data can be used as an attribute information of the point cloud data, and the attribute information can be output with the point cloud data, for example, transmitted to the upper layer The algorithm allows the upper-level algorithm to obtain more input information for further analysis by the upper-level algorithm.

In the embodiment of the present invention, the processor 430 may analyze the point cloud data to determine the type of the measured point to which the point cloud data belongs. The type of measured point can indicate the specific category of the measured point, such as sky point, ground point, vegetation point, water area point, road sign point, etc.; or, the measured point can be distinguished from other dimensions, such as dynamic point, static point, etc. . Similar to the foregoing, in the embodiment of the present invention, the measured point type to which the point cloud data belongs can be used as an attribute information of the point cloud data, and the attribute information can be output along with the point cloud data, for example, transmitted to the upper algorithm , So that the upper-level algorithm can obtain more input information for further analysis by the upper-level algorithm.

In an embodiment of the present invention, the processor 430 may analyze the point cloud data to determine the type of noise to which the point cloud data belongs. Since there are inevitably various noise points in the point cloud data (such as photoelectric noise, crosstalk noise, dust noise, rain and fog noise, etc.), these noise points are mixed in the point cloud information, as the input of the upper layer algorithm, will cause the upper layer algorithm to misjudge ; In addition, if the noise is directly filtered in the underlying algorithm, there may be a lot of false filtering and missing filtering problems due to the limitations of the underlying algorithm. Based on this, if a point cloud data is likely to be noise after analysis, then in the embodiment of the present invention, the coordinate information of the point is not directly set to zero and the point is filtered out, but the specific type of the noise is further analyzed and identified , Such as rain and fog noise, light noise, electrical noise, crosstalk noise, dust noise, etc. Similar to the foregoing, in the embodiment of the present invention, the type of noise to which the point cloud data belongs can be used as a piece of attribute information of the point cloud data. The attribute information can be output with the point cloud data, for example, transmitted to the upper-level algorithm to It is used for further analysis of the upper-level algorithm to avoid the problem of misfiltering due to analysis and recognition errors, and also to prevent the upper-level algorithm from losing other information (such as direction information, etc.) at this point.

In an embodiment of the present invention, the processor 430 may analyze the point cloud data to determine the noise confidence of the point cloud data. The noise confidence level can identify the probability that a point cloud point is a noise point. A value between 0 and 1 can be used to represent the noise point confidence level, or other different gear levels can be used to represent the noise point confidence level. Similar to the foregoing, in the embodiment of the present invention, the noise confidence of the point cloud data can be used as a piece of attribute information of the point cloud data, and the attribute information can be output with the point cloud data, for example, transmitted to the upper layer algorithm to It is used for further analysis of the upper-level algorithm, which can avoid the problems of leaky filtering and false filtering.

In the embodiment of the present invention, the processor 430 may analyze the point cloud data to determine part or all of the above-mentioned attribute information of the point cloud data, and part or all of the above-mentioned attribute information may be output together with the point cloud data, for example It is sent to the upper-layer algorithm so that the upper-layer algorithm can obtain more input information for further analysis by the upper-layer algorithm. In other embodiments, the point cloud data can also be analyzed to determine other attribute information of the point cloud data, and other attribute information can also be output along with the point cloud data, for example, transmitted to the upper-level algorithm, so that the upper-level algorithm can obtain more information. The input information can be used in the upper algorithm for further analysis.

In an embodiment of the present invention, the processor 430 may use the aforementioned various attribute information determined by analyzing the point cloud data as a piece of tag information of the point cloud data. In another embodiment of the present invention, the processor 430 may use all item attribute information determined by analyzing the point cloud data as one item of tag information of the point cloud data. The processor 430 may write the above-mentioned tag information into a data field (such as a tag information field) of a data frame for output. Exemplarily, the format of the tag information may be configured based on the principle of minimizing storage.

The following description takes all item attribute information as one item of tag information of point cloud data as an example. The processor 430 may configure the number of bits or bytes of the tag information field according to the attributes indicated by the various attribute information included in the tag information. Taking the aforementioned attribute information as an example, assuming that the number of echoes recorded by the point cloud data is 4, the processor 430 can allocate two bits to identify the number of echoes recorded by the point cloud data. Similarly, assuming that the type of noise to which the point cloud data belongs does not exceed 4, the processor 430 may allocate two bits to identify the type of noise to which the point cloud data belongs. The number of bits or bytes allocated to the rest of the attribute information is also based on this principle, and no examples are given here.

Exemplarily, the tag information field of the point cloud data may include 1 byte of data. Exemplarily, each two digits of the tag information field form a group of data from low to high, and each group of data represents one item of attribute information in the tag information. Exemplarily, the first set of data of the tag information field is used to indicate the point attributes based on spatial location, the second set of data is used to indicate the point attributes based on echo intensity, and the third set of data is used to indicate echo sequence numbers. Exemplarily, the point attributes based on the spatial location include normal points, first-level noise points, second-level noise points, and third-level noise points. The higher the noise point level, the stronger the noise filtering strength. Exemplarily, the point attributes based on echo intensity include: normal points, first-level noise points, and second-level noise points. The larger the noise point level, the stronger the echo energy and the stronger the noise filtering intensity. The example of the tag information field of the point cloud data according to the embodiment of the present invention can be understood in conjunction with Table 1. For the sake of brevity, details are not repeated here.

It should be understood that the tag information fields shown in Table 1 are only exemplary. In other examples, the size of the tag information field of the point cloud data, the number of data bits occupied by each attribute information, and the order of priority, etc., can all be other Happening.

In the embodiment of the present invention, the processor 430 may also be used to calculate and output the position information, reflectance information, and time stamp information of the point cloud data. Further, in combination with the foregoing description, the processor 430 may write the position information, reflectance information, time stamp information, and tag information of the point cloud data into the position information field, reflectance information field, and time stamp information field of a data frame, respectively. And the label information field, and output the data frame, for example, output to the upper algorithm. The upper-level algorithm can selectively use the above-mentioned information of the point cloud data according to the needs, which is beneficial to the compatibility and expansion of the upper-level algorithm.

Based on the above description, the point cloud detection system according to the embodiment of the present invention analyzes the attribute information of the point cloud data and outputs the point cloud data including the attribute information, which can retain the information obtained by the point cloud detection system to the greatest extent and eliminate The output information is blank due to the false filtering of noise at the bottom layer, which is beneficial to improve the processing effect of the upper layer algorithms (such as filtering, recognition, and segmentation algorithms).

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

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

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

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

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

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

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

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

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

The above are only specific implementations or descriptions of specific implementations of the present invention. The protection scope of the present invention is not limited thereto. Any person skilled in the art can easily fall within the technical scope disclosed by the present invention. Any change or replacement should be included in the protection scope of the present invention. The protection scope of the present invention shall be subject to the protection scope of the claims.

Claims

A signal processing method of a point cloud detection system, characterized in that the method includes:

Transmitting an optical pulse signal, and receiving an echo signal corresponding to the optical pulse signal;

The point cloud data is obtained based on the echo signal, and the point cloud data is analyzed to determine at least one of the following attributes of the point cloud data: the number of recorded echoes, the shape of the recorded echoes, and the measurement to which they belong Point type, noise point type to which it belongs, and noise confidence level; and

Output point cloud data including the result of the analysis.
The method according to claim 1, wherein each item of attribute information in the analysis result is used as a piece of tag information of the point cloud data.
The method according to claim 1, wherein all item attribute information in the analysis result is used as one item of tag information of the point cloud data.
The method according to claim 2 or 3, wherein the format of the tag information is configured based on the principle of minimizing storage.
The method according to claim 2 or 3, wherein the method further comprises:

Calculate and output the position information, reflectance information and time stamp information of the point cloud data.
The method according to claim 5, wherein the output includes point cloud data of the result of the analysis, including:

Write the point cloud data including the analysis result into the data field of a data frame, and output the data frame. The data frame includes a position information field, a reflectance information field, a time stamp information field, and a tag Information field.
The method according to claim 6, wherein the tag information field includes 1 byte of data.
The method according to claim 7, wherein the tag information field forms a set of data from low to high bits every two bits, and each set of data represents one item of attribute information in the tag information.
The method according to claim 8, wherein the first set of data in the tag information field is used to represent point attributes based on spatial location, the second set of data is used to represent point attributes based on echo intensity, and the third set of data is used to represent point attributes based on echo intensity. The group data is used to represent the echo sequence number.
The method according to claim 9, wherein the point attributes based on the spatial position include: normal points, first-level noise points, second-level noise points, and third-level noise points,

The higher the noise level, the stronger the noise filtering.
The method according to claim 9, characterized in that the point attributes based on echo intensity include: normal points, first-level noise and second-level noise, and the larger the noise level, the stronger the echo energy and the filtering of noise. The stronger the intensity.
The method according to claim 1, wherein the type of noise includes at least one of the following:

Rain and fog noise, light noise, electrical noise, crosstalk noise, dust noise.
The method according to claim 1, wherein the type of the measured point includes at least one of the following:

Sky point, ground point, vegetation point, water area point, street sign point.
The method according to claim 1, wherein the type of the measured point includes a dynamic point and a static point.
A point cloud detection system, characterized in that the system includes:

Transmitter equipment, used to transmit optical pulse signals;

The receiving end device is used to receive the echo signal corresponding to the optical pulse signal; and

The processor is configured to obtain point cloud data based on the echo signal, and analyze the point cloud data to determine at least one of the following attributes of the point cloud data: the number of recorded echoes, and the shape of the recorded echo , The type of the measured point to which it belongs, the type of the noise to which it belongs, and the confidence of the noise, and output the point cloud data including the analysis result.
The system according to claim 15, wherein each item of attribute information in the analysis result is used as a piece of tag information of the point cloud data.
The system according to claim 15, wherein all item attribute information in the analysis result is used as one item of tag information of the point cloud data.
The system according to claim 16 or 17, wherein the format of the tag information is configured based on the principle of minimizing storage.
The system according to claim 16 or 17, wherein the processor is further configured to:

Calculate and output the position information, reflectance information and time stamp information of the point cloud data.
The system according to claim 19, wherein the processor is further configured to:

Write the point cloud data including the analysis result into the data field of a data frame, and output the data frame. The data frame includes a position information field, a reflectance information field, a time stamp information field, and a tag Information field.
The system according to claim 20, wherein the tag information field includes 1 byte of data.
The system according to claim 21, wherein each two bits of the tag information field form a group of data from low to high, and each group of data represents one item of attribute information in the tag information.
The system according to claim 22, wherein the first set of data in the tag information field is used to represent point attributes based on spatial location, the second set of data is used to represent point attributes based on echo intensity, and the third set of data is used to represent point attributes based on echo intensity. The group data is used to represent the echo sequence number.
The system according to claim 23, wherein the point attributes based on spatial location include: normal points, first-level noise, second-level noise, and third-level noise, and the higher the noise level, the higher the noise filtering strength. Strong.
The system according to claim 23, wherein the point attributes based on echo intensity include: normal points, first-level noise, and second-level noise. The higher the noise level, the stronger the echo energy and the filtering of noise. The stronger the intensity.
The system according to claim 15, wherein the type of noise includes at least one of the following:

Rain and fog noise, light noise, electrical noise, crosstalk noise, dust noise.
The system according to claim 15, wherein the type of the measured point includes at least one of the following:

Sky point, ground point, vegetation point, water area point, street sign point.
The system according to claim 15, wherein the type of the measured point includes a dynamic point and a static point.