Modeling algorithm and device of laser radar point cloud detection processing system

Abstract

The invention relates to the technical field of signal processing, and discloses a modeling algorithm and device of a laser radar point cloud detection processing system. The algorithm comprises the following steps: determining a plurality of incident points according to a light source point of the laser radar and the direction of an incident light signal; calculating a first light intensity ratio of the primary reflection light signal to the incident light signal and a second light intensity ratio of the multiple reflection light signal to the incident light signal according to the incident point on each reflection surface; calculating a first receiving signal after primary reflection of the laser radar according to the first light intensity ratio, and calculating a second receiving signal after multiple reflection of the laser radar according to the second light intensity ratio; obtaining a superposed signal according to the first receiving signal, the second receiving signal and a noise signal of the laser radar; and carrying out peak detection on the superposed signal to obtain the flight time and the flight distance of the incident light signal, and obtaining point cloud data according to the flight time and the flight distance of the incident light signal. According to the invention, the modeling accuracy of laser radar transmission and signal receiving can be effectively improved.

Description

Technical Field

[0001] The present invention relates to the field of signal processing technology, and in particular to a modeling algorithm and device for a laser radar point cloud detection and processing system. Background Art

[0002] Testing the system in a simulation environment has the advantages of low cost, safety, and easy scalability. The simulation results of LiDAR can provide low-cost training data for point cloud semantic segmentation and target recognition models. In order to reduce the difference with the real scene, the undesirable factors in the process of LiDAR obtaining point clouds from the target scene also need to be added to its modeling.

[0003] However, existing lidar modeling technology has technical problems such as over-simplifying the ranging noise mechanism and not considering missing points and multiple reflections. Summary of the invention

[0004] In view of this, the purpose of the present invention is to overcome the deficiencies in the prior art and to provide a modeling algorithm and device for a laser radar point cloud detection and processing system.

[0005] The present invention provides the following technical solutions:

[0006] In a first aspect, an embodiment of the present disclosure provides a modeling algorithm for a laser radar point cloud detection and processing system, which is applied to the laser radar point cloud detection and processing system. The system includes a laser radar and a target scene. The algorithm includes:

[0007] Determining incident points on multiple reflective surfaces in the target scene according to the light source point of the laser radar and the direction of the incident light signal;

[0008] Using a bidirectional reflection distribution function and according to the incident points on each of the reflection surfaces, a first light intensity ratio of a single reflected light signal to the incident light signal is calculated, and a second light intensity ratio of a multi-reflected light signal to the incident light signal is calculated;

[0009] Calculate a first received signal after one reflection of the laser radar according to the first light intensity ratio, and calculate a second received signal after multiple reflections of the laser radar according to the second light intensity ratio;

[0010] Obtaining a superimposed signal according to the first received signal, the second received signal, and a noise signal of the laser radar;

[0011] A sliding window method is used to perform peak detection on the superimposed signal to obtain the flight time and flight distance of the incident light signal, and point cloud data output by the laser radar is obtained according to the flight time and flight distance of the incident light signal.

[0012] Optionally, determining the incident points on multiple reflective surfaces in the target scene according to the light source point of the laser radar and the direction of the incident light signal includes:

[0013] Determine the intersection of the incident light signal and each of the reflection surfaces as the incident point; or

[0014] The incident point is obtained using a ray tracing engine.

[0015] Optionally, calculating a first light intensity ratio of the primary reflected light signal and the incident light signal comprises:

[0016] Obtaining a preset total reflectivity, a preset diffuse reflection ratio, a preset specular reflection ratio, and a preset reflective surface roughness of the reflective surface where the incident point is reflected once;

[0017] Calculate a first light intensity ratio from the incident point to the light source point by using the bidirectional reflectance distribution function and according to a preset total reflectivity, a preset diffuse reflection ratio, a preset specular reflection ratio and the preset reflective surface roughness;

[0018] Wherein, the bidirectional reflectance distribution function is:

[0019]

[0020] In the formula, I o is the light intensity of the once reflected light signal, I i is the light intensity of the incident light signal, ρ is the preset total reflectivity, k d is the preset diffuse reflection ratio, k s is the preset specular reflection ratio, is the direction vector of the incident light signal, n j is the normal vector of the reflection surface where the incident point is reflected once, is the direction vector corresponding to the bisector of the incident angle and the reflected angle.

[0021] Optionally, the calculating the second light intensity ratio of the multi-reflected light signal and the incident light signal further includes:

[0022] For each of the incident points, calculating a mirror reflection direction vector, and using the ray tracing engine to obtain the reflected light signal and a new incident point of the target scene from each of the incident points and its corresponding mirror reflection direction vector;

[0023] Using the bidirectional reflectance distribution function, calculate the first reflected light signal intensity ratio from the light source point to the incident point, calculate the second reflected light signal intensity ratio from the incident point to the corresponding new incident point, and calculate the third reflected light signal intensity ratio from the incident point to the light source point;

[0024] The second light intensity ratio is calculated according to the first reflected light signal intensity ratio, the second reflected light signal intensity ratio and the third reflected light signal intensity ratio.

[0025] Optionally, the calculating of the first received signal after one reflection of the laser radar according to the first light intensity ratio, and the calculating of the second received signal after multiple reflections of the laser radar according to the second light intensity ratio include:

[0026] Acquire the pulse signal emitted by the laser radar, acquire the distance from the incident point to the laser radar, the additional flight distance of multiple reflections, the preset system efficiency of the laser radar, the preset receiver aperture and the preset aerosol attenuation;

[0027] Calculate a first received signal after one reflection of the laser radar according to the pulse signal, the distance from the incident point to the laser radar, a preset system efficiency of the laser radar, a preset receiver aperture, a preset aerosol attenuation and the first light intensity ratio;

[0028] The second received signal after multiple reflections of the laser radar is calculated based on the pulse signal, the distance from the incident point to the laser radar, the additional flight distance of the multiple reflections, the preset system efficiency of the laser radar, the preset receiver aperture, the preset aerosol attenuation and the second light intensity ratio.

[0029] Optionally, the laser radar includes a light detector, and obtaining a superposition signal according to the first received signal, the second received signal, and a noise signal of the laser radar includes:

[0030] Acquire quantum noise, dark current noise and load thermal noise of the photodetector, and model the quantum noise, the dark current noise and the load thermal noise as noise signals of the laser radar;

[0031] The sum of the first received signal, the second received signal and the noise signal of the laser radar is used as the superimposed signal.

[0032] Optionally, the adopting a sliding window method to perform peak detection on the superimposed signal to obtain the flight time and flight distance of the incident light signal, and obtaining the point cloud data output by the laser radar according to the flight time and flight distance of the incident light signal, includes:

[0033] Performing peak detection on the superimposed signal using a sliding window method to obtain a peak value of the superimposed signal;

[0034] Obtaining a peak value of the pulse signal, and taking a difference between a peak value of the superimposed signal and a peak value of the pulse signal as a flight time of the incident light signal;

[0035] The flight distance of the incident light signal in each direction is calculated according to the flight time of the incident light signal, and the laser radar is used to generate point cloud data containing all reflected light signals.

[0036] In a second aspect, an embodiment of the present disclosure provides a modeling device for a laser radar point cloud detection and processing system, which is applied to the laser radar point cloud detection and processing system. The system includes a laser radar and a target scene. The device includes:

[0037] A determination module, used to determine the incident points on multiple reflective surfaces in the target scene according to the light source point of the laser radar and the direction of the incident light signal;

[0038] A first calculation module, used for calculating a first light intensity ratio of a single reflected light signal to the incident light signal, and calculating a second light intensity ratio of a multi-reflected light signal to the incident light signal, by using a bidirectional reflectance distribution function and according to the incident points on each of the reflection surfaces;

[0039] A second calculation module is used to calculate a first received signal after a single reflection of the laser radar according to the first light intensity ratio, and to calculate a second received signal after multiple reflections of the laser radar according to the second light intensity ratio;

[0040] A superposition module, used for obtaining a superposition signal according to the first received signal, the second received signal and the noise signal of the laser radar;

[0041] The detection module is used to perform peak detection on the superimposed signal using a sliding window method to obtain the flight time and flight distance of the incident light signal, and obtain the point cloud data output by the laser radar according to the flight time and flight distance of the incident light signal.

[0042] Optionally, the first calculation module includes:

[0043] A first acquisition submodule is used to acquire a preset total reflectivity, a preset diffuse reflection ratio, a preset specular reflection ratio, and a preset reflective surface roughness of the reflective surface where the incident point is reflected once;

[0044] A first calculation submodule, used to calculate a first light intensity ratio from the incident point to the light source point by using the bidirectional reflectance distribution function and according to a preset total reflectivity, a preset diffuse reflection ratio, a preset specular reflection ratio and the preset reflective surface roughness;

[0045] Wherein, the bidirectional reflectance distribution function is:

[0046]

[0047] In the formula, I o is the light intensity of the once reflected light signal, Ii is the light intensity of the incident light signal, ρ is the preset total reflectivity, k d is the preset diffuse reflection ratio, k s is the preset specular reflection ratio, is the direction vector of the incident light signal, n j is the normal vector of the reflection surface where the incident point is reflected once, is the direction vector corresponding to the bisector of the incident angle and the reflected angle.

[0048] Optionally, the first calculation module further includes:

[0049] A second acquisition submodule is used to calculate the mirror reflection direction vector for each incident point, and use a ray tracing engine to obtain the reflected light signal and a new incident point of the target scene from each incident point and its corresponding mirror reflection direction vector;

[0050] A second calculation submodule is used to calculate the first reflected light signal intensity ratio from the light source point to the incident point, calculate the second reflected light signal intensity ratio from the incident point to the corresponding new incident point, and calculate the third reflected light signal intensity ratio from the incident point to the light source point using the bidirectional reflectance distribution function;

[0051] The third calculation submodule is used to calculate the second light intensity ratio according to the first reflected light signal intensity ratio, the second reflected light signal intensity ratio and the third reflected light signal intensity ratio.

[0052] Beneficial effects of this application:

[0053] The modeling algorithm of the laser radar point cloud detection and processing system provided in the embodiment of the present application includes: determining the incident points on multiple reflection surfaces in the target scene according to the direction of the light source point of the laser radar and the incident light signal; using the bidirectional reflection distribution function and according to the incident points on each of the reflection surfaces, calculating the first light intensity ratio of the once reflected light signal and the incident light signal, and calculating the second light intensity ratio of the multiple reflected light signal and the incident light signal; calculating the first received signal after the laser radar is reflected once according to the first light intensity ratio, and calculating the second received signal after multiple reflections of the laser radar according to the second light intensity ratio; obtaining a superposition signal according to the first received signal, the second received signal and the noise signal of the laser radar; using the sliding window method to perform peak detection on the superposition signal to obtain the flight time and flight distance of the incident light signal, and obtaining the point cloud data output by the laser radar according to the flight time and flight distance of the incident light signal. The present application can effectively improve the modeling accuracy of laser radar transmission and reception signals.

[0054] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and understandable, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments. It should be understood that the following drawings only illustrate certain embodiments of the present invention and should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work. In each of the drawings, similar components are numbered similarly.

[0056] Figure 1 A flow chart showing a modeling algorithm of a laser radar point cloud detection and processing system provided in an embodiment of the present application is shown;

[0057] Figure 2 A schematic diagram of a light path of a single reflection provided in an embodiment of the present application is shown;

[0058] Figure 3 A schematic diagram of a light path with multiple reflections provided in an embodiment of the present application is shown;

[0059] Figure 4 A waveform diagram of a superimposed signal provided in an embodiment of the present application is shown;

[0060] Figure 5 A schematic diagram of a signal point cloud of a single reflection provided by an embodiment of the present application is shown;

[0061] Figure 6 A schematic diagram of a signal point cloud of a single reflection and multiple reflections superimposed provided by an embodiment of the present application is shown;

[0062] Figure 7 A schematic diagram of a superimposed reflection signal point cloud with a noisy signal provided in an embodiment of the present application is shown;

[0063] Figure 8 A structural schematic diagram of a modeling device of a laser radar point cloud detection and processing system provided in an embodiment of the present application is shown. DETAILED DESCRIPTION

[0064] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and cannot be understood as limiting the present invention.

[0065] It should be noted that when an element is referred to as being "fixed to" another element, it may be directly on the other element or there may be a central element. When an element is considered to be "connected to" another element, it may be directly connected to the other element or there may be a central element at the same time. In contrast, when an element is referred to as being "directly on" another element, there is no intermediate element. The terms "vertical", "horizontal", "left", "right" and similar expressions used herein are for illustrative purposes only.

[0066] In the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", "connected", "fixed" and the like should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, it can be the internal connection of two elements or the interaction relationship between two elements. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0067] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0068] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application belongs. The terms used herein in the specification of the template are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The term "and / or" used herein includes any and all combinations of one or more of the related listed items.

[0069] Example 1

[0070] like Figure 1 As shown, it is a flow chart of a modeling algorithm of a laser radar point cloud detection and processing system in an embodiment of the present application. The modeling algorithm of the laser radar point cloud detection and processing system provided in the embodiment of the present application is applied to the laser radar point cloud detection and processing system. The system includes a laser radar and a target scene, and specifically includes the following steps:

[0071] Step S110, determining the incident points on multiple reflective surfaces in the target scene according to the light source point of the laser radar and the direction of the incident light signal.

[0072] Specifically, the light source point p of the laser radar s The incident light signal is emitted with a specific direction vector The target scene is composed of multiple reflective surfaces {s j}, each reflection surface can be regarded as a set of one or more triangles. The intersection of the incident light signal and the reflection surface is the incident point {p i}, this intersection point needs to satisfy the condition that it is inside the corresponding triangle. Or use the existing ray tracing engine to quickly obtain all the incident points.

[0073] Both methods can effectively determine the incident point, providing a basis for the subsequent calculation of the light intensity ratio and the received signal. Among them, the use of the ray tracing engine can more accurately simulate the propagation path of light and improve the accuracy of the calculation.

[0074] Step S120, using the bidirectional reflectance distribution function and according to the incident points on each reflection surface, calculate the first light intensity ratio of the once reflected light signal and the incident light signal, and calculate the second light intensity ratio of the multi-reflected light signal and the incident light signal.

[0075] Understandably, if Figure 2 As shown, a single reflection means that the reflected light is in the opposite direction to the incident light. After obtaining all the incident points, the preset total reflectivity, preset diffuse reflection ratio, preset specular reflection ratio and the preset reflective surface roughness of the reflective surface where the incident point is reflected once are obtained, and the ratio of the single reflected light signal intensity to the incident light signal intensity is calculated using the Blinn-Phong and other bidirectional reflectance distribution functions (BRDF):

[0076]

[0077] In the formula, I o is the intensity of the reflected light signal, I i is the intensity of the incident light signal, ρ is the preset total reflectivity, k d is the preset diffuse reflection ratio, k s To preset the specular reflection ratio, is the direction vector of the incident light signal, n j is the normal vector of the reflection surface where the incident point is reflected once, is the half-distance vector, that is, the direction vector corresponding to the bisector of the incident angle and the reflection angle, which is still

[0078] Due to the possible existence of diffuse reflection surfaces, the light intensity after a single reflection is no longer concentrated within the smaller solid angle that the laser radar can reach, but instead generates multiple paths corresponding to different single reflection angles in multiple reflections. Adding all of their effects will bring too high complexity. Therefore, this application only considers the case with the strongest reflectivity, that is, Figure 3 As shown, the light is emitted in the direction of mirror reflection and returns along the original path after reaching the new reflection surface.

[0079] Specifically, for each incident point {p i}, calculate the corresponding mirror reflection direction vector And use Intel(R) Embree ray tracing engine to traverse from each incident point {p i} and its corresponding mirror reflection direction vector Get the reflected light signal and the new incident point of the target scene {q i}. Then for the optical signal Each new incident point q i , find its corresponding reflection surface s k and the reflection surface normal vector n k , p is calculated by the bidirectional reflectance distribution function BRDF s →p i 、p i →q i ,q i →p i 、p i →p s The ratio of the light intensity before and after the three required reflections is:

[0080] (1) Calculate the light source point p s To the incident point p i The first reflected light signal strength ratio is:

[0081]

[0082] (2) Calculate the incident point p i To the corresponding new incident point q i The second reflected light signal strength ratio is:

[0083]

[0084] (3) Calculate the incident point p i To the light source point p s The third reflected light signal strength ratio is:

[0085]

[0086] Finally, the second light intensity ratio after multiple reflections is:

[0087]

[0088] The above method helps to more accurately simulate the reflection of light in the target scene and improve the accuracy of lidar point cloud detection by calculating the light intensity ratio of single reflection and multiple reflections.

[0089] Step S130, calculating the first receiving signal after the laser radar is reflected once according to the first light intensity ratio, and calculating the second receiving signal after the laser radar is reflected multiple times according to the second light intensity ratio.

[0090] Preferably, the pulse signal emitted by the laser radar is first obtained, which can be normalized and described as:

[0091]

[0092] In the formula, T is the half-maximum full width of the pulse.

[0093] After obtaining the distance from the incident point to the laser radar, the additional flight distance of multiple reflections, the preset system efficiency of the laser radar, the preset receiver aperture and the preset aerosol attenuation, the first received signal after the laser radar is reflected once is calculated:

[0094]

[0095] Where R is the distance from the incident point to the laser radar, is the first light intensity ratio, c is the speed of light, η is the preset system efficiency, r a To preset the receiver aperture, is the preset aerosol attenuation.

[0096] In the case of multiple reflections, the attenuation caused by the reflectivity can be multiplied separately, and the second received signal after multiple reflections is:

[0097]

[0098] In the formula, R m is the additional flight distance due to multiple reflections,

[0099] It should be noted that since the parameters of the laser radar such as the preset system efficiency and the preset receiver aperture are difficult to obtain, P r The coefficient πηr before (R,μ,t) a 2 The ratio of the intensity of the signal to the noise is taken as the equivalent signal-to-noise ratio (SNR), and can be obtained through experiments on the lidar in simple scenarios such as a flat panel.

[0100] The above method helps to more accurately evaluate the detection performance of the lidar and improve the accuracy and reliability of the point cloud data by calculating the single reflection and multiple reflection signals received by the lidar.

[0101] Step S140, obtaining a superimposed signal according to the first received signal, the second received signal and the noise signal of the laser radar.

[0102] The signal after the first received signal and the second received signal are superimposed is:

[0103] P r (t) = P r (R,μ1,t)+P r (R+R m ,μ1′μ2μ3,t)

[0104] Understandably, before peak detection, the noise N(t) introduced by the laser radar's light detector needs to be added. Since the quantum noise, dark current noise, and load thermal noise of the light detector can all be modeled as Gaussian noise signals, and SNR = 50dB is set to simulate the signal-noise relationship in actual situations. And the output signal current of the light detector is also proportional to the received optical power, N(t) can be modeled as superimposed on P r (t), the superimposed signal P′(t)=P r (t)+N(t), such as Figure 4 shown.

[0105] The above method can more accurately evaluate the impact of noise on the detection performance of LiDAR by modeling the noise signal of LiDAR (including quantum noise, dark current noise and load thermal noise) and adding it to the received signal to obtain a superposition signal. This helps to better remove noise interference in subsequent data processing and improve the accuracy and reliability of point cloud data.

[0106] In step S150, a sliding window method is used to perform peak detection on the superimposed signal to obtain the flight time and flight distance of the incident light signal, and point cloud data output by the laser radar is obtained according to the flight time and flight distance of the incident light signal.

[0107] Specifically, when detecting the peak value, SNR is used as the basis for measuring the difference between the signal and the noise, so as to accurately identify the peak position of the signal. In this embodiment, the peak value detection adopts the sliding window method, and the width is set to 1 / 4 of the full width at half maximum of the pulse. That is, when the superimposed signal P′(t)=P r When (t)+N(t) satisfies the following conditions, P′(t) is considered to be the peak value of the superimposed signal:

[0108]

[0109] Furthermore, the peak value P(t) of the pulse signal is obtained, and the difference between the peak value P′(t) of the superimposed signal and the peak value P(t) of the pulse signal is taken as the flight time Δt of the incident light signal, and then the corresponding distance is obtained.

[0110] LiDAR can construct a three-dimensional point cloud of the target scene by measuring the flight time of multiple light directions. And the corresponding distance in this direction Output the point cloud data of the LiDAR model. Each point cloud data represents a reflected light signal in the direction of a light beam. Figure 5 As shown in the figure, the signal point cloud of the superposition of one reflection and multiple reflections is as follows: Figure 6 As shown in the figure, the superimposed reflection signal point cloud with noise signal is as follows Figure 7 shown.

[0111] The above method takes into account all the non-ideal factors in the process of the laser radar obtaining point cloud data from the target scene, which can improve the accuracy of the laser radar transmission and reception signal modeling and can more accurately obtain the three-dimensional information of the target scene.

[0112] The modeling algorithm of the laser radar point cloud detection and processing system provided in the embodiment of the present application improves the accuracy and reliability of laser radar point cloud detection by comprehensively considering the effects of single reflection and multiple reflections, as well as the noise signal of the laser radar.

[0113] Example 2

[0114] like Figure 8 FIG. 8 is a schematic diagram of a structure of a modeling device 800 of a laser radar point cloud detection and processing system in an embodiment of the present application. The device is applied to a laser radar point cloud detection and processing system. The system includes a laser radar and a target scene, specifically including:

[0115] A determination module 810, configured to determine incident points on a plurality of reflective surfaces in the target scene according to the light source point of the laser radar and the direction of the incident light signal;

[0116] A first calculation module 820, used for calculating a first light intensity ratio of a single reflected light signal to the incident light signal, and calculating a second light intensity ratio of a multi-reflected light signal to the incident light signal, by using a bidirectional reflectance distribution function and according to the incident points on each of the reflection surfaces;

[0117] A second calculation module 830 is used to calculate a first received signal after a single reflection of the laser radar according to the first light intensity ratio, and to calculate a second received signal after multiple reflections of the laser radar according to the second light intensity ratio;

[0118] A superposition module 840, configured to obtain a superposition signal according to the first received signal, the second received signal and the noise signal of the laser radar;

[0119] The detection module 850 is used to perform peak detection on the superimposed signal using a sliding window method to obtain the flight time and flight distance of the incident light signal, and obtain the point cloud data output by the laser radar based on the flight time and flight distance of the incident light signal.

[0120] Optionally, the first calculation module 820 includes:

[0121] A first acquisition submodule is used to acquire a preset total reflectivity, a preset diffuse reflection ratio, a preset specular reflection ratio, and a preset reflective surface roughness of the reflective surface where the incident point is reflected once;

[0122] A first calculation submodule, used to calculate a first light intensity ratio from the incident point to the light source point by using the bidirectional reflectance distribution function and according to a preset total reflectivity, a preset diffuse reflection ratio, a preset specular reflection ratio and the preset reflective surface roughness;

[0123] Wherein, the bidirectional reflectance distribution function is:

[0124]

[0125] In the formula, I o is the light intensity of the once reflected light signal, I i is the light intensity of the incident light signal, ρ is the preset total reflectivity, k d is the preset diffuse reflection ratio, k s is the preset specular reflection ratio, is the direction vector of the incident light signal, n j is the normal vector of the reflection surface where the incident point is reflected once, is the direction vector corresponding to the bisector of the incident angle and the reflected angle.

[0126] Optionally, the first calculation module 820 further includes:

[0127] A second acquisition submodule is used to calculate the mirror reflection direction vector for each incident point, and use a ray tracing engine to obtain the reflected light signal and a new incident point of the target scene from each incident point and its corresponding mirror reflection direction vector;

[0128] A second calculation submodule is used to calculate the first reflected light signal intensity ratio from the light source point to the incident point, calculate the second reflected light signal intensity ratio from the incident point to the corresponding new incident point, and calculate the third reflected light signal intensity ratio from the incident point to the light source point using the bidirectional reflectance distribution function;

[0129] The third calculation submodule is used to calculate the second light intensity ratio according to the first reflected light signal intensity ratio, the second reflected light signal intensity ratio and the third reflected light signal intensity ratio.

[0130] The modeling device of the laser radar point cloud detection and processing system provided in the embodiment of the present application improves the accuracy and reliability of the laser radar point cloud detection by comprehensively considering the influence of single reflection and multiple reflections, as well as the noise signal of the laser radar.

[0131] In several embodiments provided in the present application, it should be understood that the disclosed devices and methods can also be implemented in other ways. The device embodiments described above are merely schematic. For example, the flowcharts and structure diagrams in the accompanying drawings show the possible architecture, functions and operations of the devices, methods and computer program products according to multiple embodiments of the present invention. In this regard, each box in the flowchart or block diagram can represent a module, a program segment or a part of a code, and the module, a program segment or a part of a code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in an alternative implementation, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the structure diagram and / or the flow chart, and the combination of boxes in the structure diagram and / or the flow chart, can be implemented with a dedicated hardware-based system that performs a specified function or action, or can be implemented with a combination of dedicated hardware and computer instructions.

[0132] In addition, the functional modules or units in the various embodiments of the present invention may be integrated together to form an independent part, or each module may exist independently, or two or more modules may be integrated to form an independent part.

[0133] If the function is implemented in the form of a software function module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention is essentially or partly contributed to the prior art or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including several instructions to enable a computer device (which can be a smart phone, a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium can be a non-volatile storage medium or a volatile storage medium. For example, the storage medium can be: a U disk, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a disk or an optical disk, and other media that can store program codes.

[0134] The above description is only a specific implementation mode of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A modeling algorithm for a laser radar point cloud detection and processing system, characterized in that: Applied to a laser radar point cloud detection and processing system, the system includes a laser radar and a target scene, and the algorithm includes: Determining incident points on multiple reflective surfaces in the target scene according to the light source point of the laser radar and the direction of the incident light signal; Using a bidirectional reflection distribution function and according to the incident points on each of the reflection surfaces, a first light intensity ratio of a single reflected light signal to the incident light signal is calculated, and a second light intensity ratio of a multi-reflected light signal to the incident light signal is calculated; Calculate a first received signal after one reflection of the laser radar according to the first light intensity ratio, and calculate a second received signal after multiple reflections of the laser radar according to the second light intensity ratio; Obtaining a superimposed signal according to the first received signal, the second received signal, and a noise signal of the laser radar; A sliding window method is used to perform peak detection on the superimposed signal to obtain the flight time and flight distance of the incident light signal, and point cloud data output by the laser radar is obtained according to the flight time and flight distance of the incident light signal.

2. The modeling algorithm of the laser radar point cloud detection and processing system according to claim 1 is characterized in that: The step of determining the incident points on the multiple reflecting surfaces in the target scene according to the light source point of the laser radar and the direction of the incident light signal comprises: Determine the intersection of the incident light signal and each of the reflection surfaces as the incident point; or The incident point is obtained using a ray tracing engine.

3. The modeling algorithm of the laser radar point cloud detection and processing system according to claim 1 is characterized in that: The calculating a first light intensity ratio of the primary reflected light signal and the incident light signal comprises: Obtaining a preset total reflectivity, a preset diffuse reflection ratio, a preset specular reflection ratio, and a preset reflective surface roughness of the reflective surface where the incident point is reflected once; Calculate a first light intensity ratio from the incident point to the light source point by using the bidirectional reflectance distribution function and according to a preset total reflectivity, a preset diffuse reflection ratio, a preset specular reflection ratio and the preset reflective surface roughness; Wherein, the bidirectional reflectance distribution function is: In the formula, I o is the light intensity of the once reflected light signal, I i is the light intensity of the incident light signal, ρ is the preset total reflectivity, k d is the preset diffuse reflection ratio, k s is the preset specular reflection ratio, is the direction vector of the incident light signal, n j is the normal vector of the reflection surface where the incident point is reflected once, is the direction vector corresponding to the bisector of the incident angle and the reflected angle.

4. The modeling algorithm of the laser radar point cloud detection and processing system according to claim 2 is characterized in that: The calculating of the second light intensity ratio of the multi-reflected light signal and the incident light signal further comprises: For each of the incident points, calculating a mirror reflection direction vector, and using the ray tracing engine to obtain the reflected light signal and a new incident point of the target scene from each of the incident points and its corresponding mirror reflection direction vector; Using the bidirectional reflectance distribution function, calculate the first reflected light signal intensity ratio from the light source point to the incident point, calculate the second reflected light signal intensity ratio from the incident point to the corresponding new incident point, and calculate the third reflected light signal intensity ratio from the incident point to the light source point; The second light intensity ratio is calculated according to the first reflected light signal intensity ratio, the second reflected light signal intensity ratio and the third reflected light signal intensity ratio.

5. The modeling algorithm of the laser radar point cloud detection and processing system according to claim 1 is characterized in that: The step of calculating the first received signal after one reflection of the laser radar according to the first light intensity ratio, and calculating the second received signal after multiple reflections of the laser radar according to the second light intensity ratio, comprises: Acquire the pulse signal emitted by the laser radar, acquire the distance from the incident point to the laser radar, the additional flight distance of multiple reflections, the preset system efficiency of the laser radar, the preset receiver aperture and the preset aerosol attenuation; Calculate a first received signal after one reflection of the laser radar according to the pulse signal, the distance from the incident point to the laser radar, a preset system efficiency of the laser radar, a preset receiver aperture, a preset aerosol attenuation and the first light intensity ratio; The second received signal after multiple reflections of the laser radar is calculated based on the pulse signal, the distance from the incident point to the laser radar, the additional flight distance of the multiple reflections, the preset system efficiency of the laser radar, the preset receiver aperture, the preset aerosol attenuation and the second light intensity ratio.

6. The modeling algorithm of the laser radar point cloud detection and processing system according to claim 1 is characterized in that: The laser radar includes a light detector, and obtaining a superposition signal according to the first received signal, the second received signal, and a noise signal of the laser radar includes: Acquire quantum noise, dark current noise and load thermal noise of the photodetector, and model the quantum noise, the dark current noise and the load thermal noise as noise signals of the laser radar; The sum of the first received signal, the second received signal and the noise signal of the laser radar is used as the superimposed signal.

7. The modeling algorithm of the laser radar point cloud detection and processing system according to claim 5 is characterized in that: The method of using a sliding window method to perform peak detection on the superimposed signal to obtain the flight time and flight distance of the incident light signal, and obtaining the point cloud data output by the laser radar according to the flight time and flight distance of the incident light signal, includes: Performing peak detection on the superimposed signal using a sliding window method to obtain a peak value of the superimposed signal; Obtaining a peak value of the pulse signal, and taking a difference between a peak value of the superimposed signal and a peak value of the pulse signal as a flight time of the incident light signal; The flight distance of the incident light signal in each direction is calculated according to the flight time of the incident light signal, and the laser radar is used to generate point cloud data containing all reflected light signals.

8. A modeling device for a laser radar point cloud detection and processing system, characterized in that: Applied to a laser radar point cloud detection and processing system, the system includes a laser radar and a target scene, and the device includes: A determination module, used to determine the incident points on multiple reflective surfaces in the target scene according to the light source point of the laser radar and the direction of the incident light signal; A first calculation module, used for calculating a first light intensity ratio of a single reflected light signal to the incident light signal, and calculating a second light intensity ratio of a multi-reflected light signal to the incident light signal, by using a bidirectional reflectance distribution function and according to the incident points on each of the reflection surfaces; A second calculation module is used to calculate a first received signal after a single reflection of the laser radar according to the first light intensity ratio, and to calculate a second received signal after multiple reflections of the laser radar according to the second light intensity ratio; A superposition module, used for obtaining a superposition signal according to the first received signal, the second received signal and the noise signal of the laser radar; The detection module is used to perform peak detection on the superimposed signal using a sliding window method to obtain the flight time and flight distance of the incident light signal, and obtain the point cloud data output by the laser radar according to the flight time and flight distance of the incident light signal.

9. The modeling device of the laser radar point cloud detection and processing system according to claim 8, characterized in that: The first calculation module includes: A first acquisition submodule is used to acquire a preset total reflectivity, a preset diffuse reflection ratio, a preset specular reflection ratio, and a preset reflective surface roughness of the reflective surface where the incident point is reflected once; A first calculation submodule, used to calculate a first light intensity ratio from the incident point to the light source point by using the bidirectional reflectance distribution function and according to a preset total reflectivity, a preset diffuse reflection ratio, a preset specular reflection ratio and the preset reflective surface roughness; Wherein, the bidirectional reflectance distribution function is: In the formula, I o is the light intensity of the once reflected light signal, I i is the light intensity of the incident light signal, ρ is the preset total reflectivity, k d is the preset diffuse reflection ratio, k s is the preset specular reflection ratio, is the direction vector of the incident light signal, n j is the normal vector of the reflection surface where the incident point is reflected once, is the direction vector corresponding to the bisector of the incident angle and the reflected angle.

10. The modeling device of the laser radar point cloud detection and processing system according to claim 8, characterized in that: The first calculation module also includes: A second acquisition submodule is used to calculate the mirror reflection direction vector for each incident point, and use a ray tracing engine to obtain the reflected light signal and a new incident point of the target scene from each incident point and its corresponding mirror reflection direction vector; A second calculation submodule is used to calculate the first reflected light signal intensity ratio from the light source point to the incident point, calculate the second reflected light signal intensity ratio from the incident point to the corresponding new incident point, and calculate the third reflected light signal intensity ratio from the incident point to the light source point using the bidirectional reflectance distribution function; The third calculation submodule is used to calculate the second light intensity ratio according to the first reflected light signal intensity ratio, the second reflected light signal intensity ratio and the third reflected light signal intensity ratio.

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