Laser radar ranging method, laser radar, storage medium and mobile robot

Abstract

This application discloses a laser radar ranging method, a laser radar, a storage medium, and a mobile robot. The laser radar method includes: obtaining first event stream data generated by the laser radar's dynamic vision sensor based on a light signal; wherein the light signal is obtained by a light beam emitted by the laser radar's transmitting module and reflected by a target object; processing the first event stream data to obtain second event stream data; and analyzing the second event stream data to obtain a distance value from the target object to the laser radar. The above scheme can improve the efficiency and accuracy of laser radar ranging.

Description

Technical Field

[0001] The present application relates to the field of target detection technology, and in particular to a laser radar ranging method, a laser radar, a storage medium, and a mobile robot. Background Art

[0002] The continuous development of smart devices has led to higher requirements for target detection accuracy. LiDAR (LiDAR) has been widely used in target detection due to its advantages of long-range and high-accuracy ranging. However, existing LiDAR-based ranging methods significantly reduce detection efficiency and accuracy when the target image has a high pixel count or when the target is moving at high speed. Therefore, improving the efficiency and accuracy of LiDAR ranging has become an urgent issue. Summary of the Invention

[0003] The main technical problem solved by this application is to provide a laser radar ranging method, a laser radar, a storage medium and a mobile robot, which can improve the efficiency and accuracy of laser radar ranging.

[0004] To solve the above technical problems, the first aspect of the present application provides a ranging method for a laser radar, which includes: obtaining first event stream data generated by a dynamic vision sensor of the laser radar based on a light signal; wherein the light signal is obtained by reflecting a light beam emitted by an emitting module of the laser radar through a target object; processing the first event stream data to obtain second event stream data; and analyzing the second event stream data to obtain a distance value from the target object to the laser radar.

[0005] In order to solve the above technical problems, the second aspect of the present application provides a laser radar, which includes: a transmitting module, a dynamic vision sensor and a processing module, and the transmitting module and the dynamic vision sensor are both coupled to the processing module; wherein, the dynamic vision sensor is used to receive a light signal, the transmitting module is used to transmit a light beam to a target object, and the light beam is reflected by the target object to form the light signal, and the processing module is used to execute the method described in the first aspect above.

[0006] In order to solve the above technical problems, the third aspect of the present application provides a computer-readable storage medium on which program instructions are stored. When the program instructions are executed by a processor, the method described in the first aspect is implemented.

[0007] In order to solve the above technical problems, the fourth aspect of the present application provides a mobile robot, which includes a robot body and the laser radar described in the second aspect.

[0008] In the above scheme, the laser radar's transmitting module emits a light beam that is reflected by the target to form a light beam, and obtains the first event stream data generated by the laser radar's dynamic vision sensor based on the light signal. Among them, the dynamic vision sensor has a smaller data volume than the traditional image acquisition camera and will not produce motion blur due to high-speed moving targets. Therefore, the dynamic vision sensor has higher efficiency in generating the first event stream data, and can still generate accurate first event stream data for high-speed moving targets, thereby improving the accuracy of ranging for high-speed moving targets. The first event stream data is processed to extract the second event stream data from the first event stream data. Based on the second event stream data, analysis is performed to obtain the distance value from the target object to the laser radar. Since the second event stream data is obtained from the first event stream data, there is no need to store and process all the data to speed up the data processing speed, thereby improving the efficiency of the laser radar's ranging and saving storage space. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For those skilled in the art, other drawings can be obtained based on these drawings without inventive efforts. Among them:

[0010] Figure 1 This is a flow chart of an embodiment of a ranging method of a laser radar of the present application;

[0011] Figure 2 yes Figure 1 Schematic diagram of an application scenario of an embodiment corresponding to step S101;

[0012] Figure 3 yes Figure 1 Schematic diagram of an application scenario of an embodiment corresponding to step S102;

[0013] Figure 4 This is a flow chart of another embodiment of the ranging method of the laser radar of the present application;

[0014] Figure 5 yes Figure 4 Flowchart of an embodiment corresponding to step S402

[0015] Figure 6 yes Figure 5 Schematic diagram of an application scenario of an embodiment corresponding to step S502;

[0016] Figure 7 This is a schematic diagram of the topological structure of an embodiment of the laser radar of the present application;

[0017] Figure 8 It is a topological diagram of an embodiment of a computer-readable storage medium of the present application;

[0018] Figure 9 This is a topological diagram of an embodiment of the mobile robot of the present application. DETAILED DESCRIPTION

[0019] The following will be combined with the accompanying drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of them. Based on the embodiments of this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0020] The terms "system" and "network" are often used interchangeably in this document. The term "and / or" is simply a description of an association between related objects, indicating that three possible relationships exist. For example, "A and / or B" can mean: A exists alone, A and B exist simultaneously, or B exists alone. Furthermore, the character " / " generally indicates that the related objects are in an "or" relationship. Furthermore, "multiple" in this document means two or more than two.

[0021] See also Figure 1 , Figure 1 This is a flow chart of an embodiment of a ranging method for a laser radar of the present application, the method comprising:

[0022] S101: Acquire first event stream data generated by a dynamic vision sensor of a laser radar based on a light signal, wherein the light signal is obtained by a light beam emitted by a transmitting module of the laser radar and reflected by a target object.

[0023] Specifically, the laser radar includes a transmitting module and a dynamic vision sensor. The transmitting module emits a light beam outward for detecting the target object. When the light beam reaches the target object and is reflected by the target object, a light signal is reflected back. The dynamic vision sensor of the laser radar receives the light signal and generates a first event stream data based on the light signal, thereby obtaining the first event stream data generated by the dynamic vision sensor of the laser radar based on the light signal.

[0024] Furthermore, the Dynamic Vision Sensor (DVS) can output an event after the cumulative brightness change of the pixel reaches a certain threshold, that is, it outputs the change in the brightness of the pixel. The result output by the dynamic vision sensor has nothing to do with the state of the object in the picture, but is related to the brightness change of the pixels in the picture. Therefore, the dynamic vision sensor is different from the traditional CMOS sensor or CCD sensor. The traditional visual image acquisition method is based on the "frame" acquired at a fixed frequency, which has defects such as high redundancy, high latency, high noise, low dynamic range and high data volume. The dynamic vision sensor works asynchronously with pixels and only outputs the address and information of the pixel where the light intensity changes, rather than passively reading out the information of each pixel in the "frame" in turn, eliminating redundant data from the source, and has the characteristics of real-time dynamic response to scene changes, ultra-sparse image representation, and asynchronous output of events.

[0025] In one application scenario, the dynamic vision sensor is an event camera. An event camera is a camera that triggers a signal only when the brightness of pixels in the lens changes. Traditional cameras capture images at a fixed frame rate. Traditional cameras will produce overexposure when imaging scenes with a high dynamic range and motion blur when imaging high-speed moving targets. The dynamic range of an event camera reaches 140dB, which has a high dynamic range. The event camera will not produce motion blur when collecting data on high-speed moving targets. The event camera outputs the brightness change result of the pixel brightness based on time changes. Therefore, the output result of the event camera is independent of the absolute value of the pixel brightness and has better anti-interference performance.

[0026] In one application scenario, see Figure 2 , Figure 2 yes Figure 1 Schematic diagram of an application scenario of an embodiment corresponding to step S101 in the embodiment, obtaining the first event stream data generated based on the light signal after the dynamic vision sensor of the laser radar receives the light signal, the schematic diagram corresponding to the first event stream data is as follows Figure 2 As shown, the laser radar's transmitting module transmits a light beam according to a preset modulation frequency. For a moving target object, the dynamic vision sensor can capture the brightness change of the moving target object. For a stationary target object, the reflected light signal will also have brightness changes due to the light beam emitted based on the modulation frequency. Then, after receiving the light signal, the dynamic vision sensor can generate the following image based on the light signal: Figure 2 The first event stream data shown in FIG. The first event stream data element is composed of (t, x, y, r), where t is the timestamp, x is the horizontal coordinate of the pixel point in the dynamic vision sensor, y is the vertical coordinate of the pixel point in the dynamic vision sensor, and r represents the increase or decrease of brightness.

[0027] S102: Process the first event stream data to obtain second event stream data.

[0028] Specifically, sampling is performed in the first event stream data to extract part of the data in the first event stream data or data features corresponding to the data to obtain the second event stream data, so that the data volume of the second event stream data is significantly reduced relative to the data volume of the first event stream data, thereby eliminating the need to store and process all data to speed up data processing.

[0029] In one application mode, part of the data in the first event stream data is extracted according to a preset time interval, see Figure 3 , Figure 3 yes Figure 1 Schematic diagram of an application scenario of an embodiment corresponding to step S102, wherein Δt is a preset time interval, sampling is performed in the first event stream data according to the preset time interval, corresponding data is extracted from the first event stream data after each preset time interval, the extracted data is mapped so that the extracted data is mapped to the coordinate system corresponding to the dynamic vision sensor, and second event stream data generated based on brightness changes in the first event stream data is obtained.

[0030] In another application method, sampling is performed in the first event stream data at a certain rotation angle or within a certain rotation angle range, corresponding data is extracted from the first event stream data, and the extracted data is mapped so that the extracted data is mapped to the coordinate system corresponding to the dynamic vision sensor, thereby obtaining second event stream data generated based on the angle change in the first event stream data.

[0031] S103: Analyze the second event stream data to obtain a distance value from the target object to the laser radar.

[0032] Specifically, the second event stream data is analyzed, and the position of the target object is determined based on the data in the second event stream data, thereby calculating and obtaining the distance value of the target object relative to the laser radar based on the triangulation principle.

[0033] In one application, in response to obtaining multiple data in the second event stream data, the data in the second event stream data are weighted and summed to obtain the coordinates of the target object, thereby obtaining the distance value of the target object relative to the lidar based on triangulation distance calculation.

[0034] In one application scenario, the coordinates of multiple data points in a coordinate system corresponding to a dynamic vision sensor are obtained from the second event stream data. Each coordinate represents the position of a target object at each preset time interval. Assuming that n coordinates are obtained from the second event stream data, namely P1, P2, ..., and Pn, a weighted sum of all n coordinates is performed to obtain P^=P1*a1+P2*a2+...+Pn*an. The sum of the weights corresponding to all coordinates is 1, that is, a1+a2+...+an=1.

[0035] In the above scheme, the laser radar's transmitting module emits a light beam that is reflected by the target to form a light beam, and obtains the first event stream data generated by the laser radar's dynamic vision sensor based on the light signal. Among them, the dynamic vision sensor has a smaller data volume than the traditional image acquisition camera and will not produce motion blur due to high-speed moving targets. Therefore, the dynamic vision sensor has higher efficiency in generating the first event stream data, and can still generate accurate first event stream data for high-speed moving targets, thereby improving the accuracy of ranging for high-speed moving targets. The first event stream data is processed to extract the second event stream data from the first event stream data. Based on the second event stream data, analysis is performed to obtain the distance value from the target object to the laser radar. Since the second event stream data is obtained from the first event stream data, there is no need to store and process all the data to speed up the data processing speed, thereby improving the efficiency of the laser radar's ranging and saving storage space.

[0036] See also Figure 4 , Figure 4 : is a flow chart of another embodiment of the ranging method of the laser radar of the present application, the method comprising:

[0037] S401: Acquire first event stream data generated by a dynamic vision sensor of a laser radar based on a light signal, wherein the light signal is obtained by a light beam emitted by a transmitting module of the laser radar and reflected by a target object.

[0038] Specifically, the transmitting module of the laser radar emits a light beam with a preset modulation frequency. When the light beam hits the target object, it is reflected by the target object to obtain a light signal. The light signal is received by the dynamic vision sensor to generate the first event stream data.

[0039] In one application, the transmitting module transmits a light beam with at least one modulation frequency, and the preset frequency interval or the preset time interval is related to the value of the modulation frequency.

[0040] Specifically, the transmitting module can emit a light beam with a constant modulation frequency, so as to meet the detection of target objects in a scenario with a certain specific frequency, so as to reduce the difficulty of data analysis and processing. The transmitting module can also emit light beams with multiple modulation frequencies to meet the needs of scenarios with multiple target objects.

[0041] Furthermore, the preset frequency interval or preset time interval for subsequent sampling of the first event stream data is correlated with the value of the modulation frequency, and generally, is negatively correlated.

[0042] In one application scenario, when the transmitting module emits a light beam with a single modulation frequency, the preset time interval is the inverse of the modulation frequency. When the transmitting module emits light beams with multiple modulation frequencies, when sampling the first event stream data, the preset time interval changes accordingly according to the change in the modulation frequency, so that the preset time interval always remains the inverse of the modulation frequency.

[0043] In another application, when the transmitting module transmits light beams with multiple modulation frequencies, the transmitting module transmits light beams with different modulation frequencies alternately.

[0044] Specifically, when the transmitting module emits light beams with multiple modulation frequencies, the light beams with different modulation frequencies are emitted alternately, thereby providing light beams with multiple different adjustment frequencies to detect target objects, so as to detect different target objects, reduce the probability of missing target objects, meet the scenarios where there are multiple different target objects or the target objects have large changes in movement speed, and at the same time improve the anti-interference performance in different application scenarios.

[0045] Furthermore, the preset frequency interval or preset time interval for subsequent sampling of the first event stream data is related to the numerical value of the modulation frequency. When the transmitting module alternately sends light beams of different modulation frequencies, the preset frequency interval or preset time interval is transformed in the same alternating manner, thereby extracting data in the first event stream data in an orderly manner.

[0046] It should be noted that after receiving the light signal, the dynamic vision sensor generates first event stream data based on the brightness changes of the target object. The light beam changes according to the modulation frequency. When the target object surface is irradiated by the light beam, the frequency of the light beam received by the target object surface changes, resulting in a certain frequency of brightness changes on the target object surface. When the target object is in motion, the brightness changes on the target object surface are more significant. The first event stream data generated by the dynamic vision sensor based on pixel brightness changes is different from the image generated based on all pixels. The first event stream data has a smaller data volume and does not need to store and process all the pixels of the target object, saving storage space and helping to speed up the lidar's data processing.

[0047] S402: Sampling the first event stream data according to a preset frequency interval or a preset time interval to obtain second event stream data.

[0048] Specifically, the first event stream data is sampled, thereby significantly reducing the data volume of the first event stream data to obtain the second event stream data. When sampling the first event stream data, downsampling of the first event stream data can be achieved based on a preset frequency interval or a preset time interval, thereby reducing the data processing volume and improving processing efficiency.

[0049] In one application, see Figure 5 , Figure 5 yes Figure 4 Schematic diagram of a flow chart of an embodiment corresponding to step S402, step S402 specifically includes:

[0050] S501: Extract multiple data features about a target dimension from the first event stream data according to a preset frequency interval or a preset time interval to obtain a data set consisting of multiple data features, wherein the target dimension includes at least one of event, brightness, frequency, position and phase.

[0051] Specifically, data features about the target dimension are extracted from the first event stream data according to a preset frequency interval or a preset time interval related to the modulation frequency, wherein the target dimension includes at least one of event, brightness, frequency, position and phase, and the extracted data features are combined into a data set.

[0052] In an application scenario, please refer to Figure 3 , extract a data feature of a target dimension from the first event stream data according to the preset time interval, and divide each preset time interval, i.e. Figure 3 The data features extracted from the position corresponding to the middle dotted line are taken as a data subset, and the various data subsets are combined into a data set to obtain a data set corresponding to a target dimension.

[0053] In another application method, data features of multiple target dimensions are extracted from the first event stream data according to a preset frequency interval, and data features corresponding to the target dimensions are extracted at the preset frequency interval according to the type of the target dimension. The data features of each target dimension at the preset frequency interval constitute a data subset, thereby obtaining a data set corresponding to each target dimension. Different data sets can be used to analyze the distance value between the target object and the laser radar, thereby reducing the discrete error of the distance value analysis and improving the accuracy of the laser radar in measuring the distance of the target object.

[0054] In a specific application scenario, multiple data features about the target dimension are extracted from the first event stream data according to the preset frequency interval or preset time interval to obtain a data set composed of multiple data features, including: extracting data features about the target dimension from the first event stream data according to the preset frequency interval or preset time interval to obtain multiple data subsets composed of the data features extracted at each preset time interval; for each data subset, taking the current data subset as the current event stream data, and taking other data subsets outside the current data subset as reference event stream data; obtaining the feature differences between the data features of the current event stream data and the data features of the reference event stream data, and counting the number of reference event stream data whose feature differences with the current event stream data exceed the preset range; based on the data quantity, selecting data subsets to form the data set.

[0055] Specifically, when the target object is in motion or the movement of part of the target object is sudden, the laser radar can screen the collected data features to capture the moving target object, so as to obtain the data features corresponding to the moving target object. The data features extracted at each preset time interval are used as a data subset. For each data subset, the current data subset is used as the current event stream data, and the data features in other data subsets are used as reference event stream data. The data features in the data subsets corresponding to the current event stream data and the reference event stream data are compared to obtain the feature difference between the two. The feature difference is compared with the preset numerical range, so as to count the number of data in the reference event stream data that exceeds the preset numerical range. If the number of data exceeds the number threshold, the data subset corresponding to the current event stream data is regarded as outlier data.

[0056] Furthermore, if the current event stream data is outlier data, the data features in the current event stream data are significantly different from the data features in most data subsets, that is, the node data features corresponding to the current event stream data have undergone significant changes, and then the data subsets with significant changes in data features are screened out, and the data subsets with changed data features are selected to form a data set, so as to detect target objects in motion, especially those in sudden motion, or, other data subsets other than the outlier data are selected to form a data set, so as to detect target objects that are stationary or in constant motion.

[0057] S502: Generate second event stream data based on each data feature in the data set.

[0058] Specifically, each data feature in the data set is mapped to the coordinate system of the dynamic vision sensor to obtain the second event stream data.

[0059] In one application, see Figure 6 , Figure 6 yes Figure 5 A schematic diagram of an application scenario of an embodiment corresponding to step S502 in FIG. 5 , mapping each data feature in the data set from the coordinate system corresponding to the first event stream data to the coordinate system corresponding to the dynamic vision sensor to generate second event stream data.

[0060] Specifically, the data features in the data set are mapped from the coordinate system corresponding to the first event stream to the coordinate system corresponding to the dynamic vision sensor to obtain the position of the data features in the coordinate system corresponding to the dynamic vision sensor, that is, the position of the target object mapped to the dynamic vision sensor under a certain target dimension, thereby generating the second event stream data to make the second event stream data concrete, which is convenient for analyzing the distance between the target object and the lidar.

[0061] S403: Based on the pixel coordinates in the second event stream data and the internal reference parameters of the dynamic vision sensor, obtain the distance value from the target object to the laser radar.

[0062] Specifically, the pixel coordinates of the second event stream data in the coordinate system corresponding to the dynamic vision sensor are extracted to obtain the position of the target object in the coordinate system corresponding to the dynamic vision sensor, the intrinsic parameters of the dynamic vision sensor are obtained, and the distance value between the target object and the lidar is calculated using the triangulation principle.

[0063] Furthermore, when a target object corresponds to multiple pixel coordinates, a weighted summation is performed on the pixel coordinates of the target object to determine the target object's position. The transmitting module transmits a light beam at a certain angle, the target object reflects the light signal, and the dynamic vision sensor receives the light signal. After determining the target object's position in the dynamic vision sensor, the distance between the target object and the lidar can be determined based on the angle at which the transmitting module transmits the light beam, the distance between the transmitting module and the focal point, and the internal parameters of the dynamic vision sensor. The internal parameters of the dynamic vision sensor include the photosensitive focal length.

[0064] In one application method, pixel coordinates other than the current pixel coordinates in the second event stream data are used as reference coordinate data. Based on the coordinate difference between the current pixel coordinates and the reference coordinate data, the weight corresponding to the current pixel coordinates in the second event stream data is obtained. The weights corresponding to each pixel coordinate in the second event stream data are used to perform weighted summation on the pixel coordinates to obtain calibration coordinates. Analysis is performed based on the calibration coordinates to obtain the distance value.

[0065] Specifically, for each pixel coordinate, the traversed pixel coordinate is used as the current pixel coordinate, and the pixel coordinates other than the current pixel coordinate are used as reference coordinate data. The current pixel coordinate is compared with the reference coordinate data to obtain the difference between the current pixel coordinate and the reference coordinate data. Based on the difference, it is judged whether the current pixel coordinate deviates greatly from other pixel coordinates. If the coordinate difference exceeds the coordinate threshold, it means that the target object on the node corresponding to the current pixel coordinate has changed greatly. A higher weight is given to the pixel coordinate with a larger change, so that the pixel points with a higher degree of change account for a larger proportion, thereby feeding back the motion state of the target object. The pixel coordinates in the second event stream data are weighted and summed to obtain the calibration coordinates. Based on the calibration coordinates and the intrinsic parameters of the dynamic vision sensor, the distance value between the target object and the lidar is calculated.

[0066] In this embodiment, the laser radar uses modulated laser to detect the target object, which has better anti-interference performance for different application scenarios. The first event stream data generated by the dynamic vision sensor based on the pixel brightness change is different from the image generated based on all pixels. The data volume of the first event stream data is smaller, and there is no need to store and process all pixels of the target object, saving storage space and speeding up data processing. The second event stream data is sampled and extracted from the first event stream data, thereby further reducing the data processing volume and improving the efficiency of ranging the target object. The pixel coordinates are extracted based on the second event stream data and weights related to the degree of change are set for the pixel coordinates to determine the calibration coordinates corresponding to the target object. The calibration coordinates are analyzed to obtain the distance value between the target object and the laser radar, thereby improving the accuracy of ranging the target object.

[0067] See also Figure 7 , Figure 7 This is a schematic diagram of the topological structure of an embodiment of the laser radar of the present application. The laser radar 70 includes a transmitting module 700, a dynamic vision sensor 702 and a processing module 704. The transmitting module 700 and the dynamic vision sensor 702 are both coupled to the processing module 704; wherein, the dynamic vision sensor 702 is used to receive light signals, the transmitting module 700 is used to transmit a light beam to the target object, and the light beam is reflected by the target object to form a light signal, and the processing module 704 is used to execute the method in any of the above embodiments. For an explanation of the relevant content, please refer to the detailed description of the above method embodiments, which will not be repeated here.

[0068] In the above scheme, the transmitting module 700 of the laser radar 70 transmits a light beam which is reflected by the target to form a light beam, and obtains the first event stream data generated by the dynamic vision sensor 702 of the laser radar 70 based on the light signal. The dynamic vision sensor 702 has a smaller data volume than the traditional image acquisition camera and will not produce motion blur due to high-speed moving targets. Therefore, the dynamic vision sensor 702 has higher efficiency in generating the first event stream data, and can still generate accurate first event stream data for high-speed moving targets, thereby improving the accuracy of ranging for high-speed moving targets. The processing module 704 processes the first event stream data, thereby extracting the second event stream data from the first event stream data. The processing module 704 analyzes the second event stream data to obtain the distance value from the target object to the laser radar 70. Since the second event stream data is obtained from the first event stream data, there is no need to store and process all the data to speed up the data processing speed, thereby improving the efficiency of the laser radar 70 in ranging and saving storage space.

[0069] See also Figure 8 , Figure 8 This is a topological structure diagram of an embodiment of a computer-readable storage medium of the present application. The computer-readable storage medium 80 stores program instructions 800. When the program instructions 800 are executed by the processor, the method in any of the above embodiments is implemented. For the description of the relevant content, please refer to the detailed description of the above method embodiments, which will not be repeated here.

[0070] See also Figure 9 , Figure 9 This is a topological diagram of an embodiment of a mobile robot of the present application. The mobile robot 90 includes a robot body 900 and a laser radar 70. The laser radar 70 is the same as the one in the above embodiment. Figure 7 The laser radar 70 shown in .

[0071] It should be noted that the units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of this embodiment.

[0072] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0073] If the integrated unit is implemented in the form of a software functional unit 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 application is essentially or the part that contributes to the prior art or all 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 a number of instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) or a processor to execute all or part of the steps of each embodiment method of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0074] The above description is only an implementation method of the present application and does not limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the description and drawings of this application, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present application.

Claims

1. A laser radar ranging method, characterized in that: include: Acquire first event stream data generated by a dynamic vision sensor of a laser radar based on a light signal; wherein the light signal is obtained by a light beam emitted by a transmitting module of the laser radar and reflected by a target object; Processing the first event stream data to obtain second event stream data; Analyze the second event stream data to obtain a distance value from the target object to the laser radar; The processing of the first event stream data to obtain second event stream data includes: Sampling the first event stream data according to a preset frequency interval or a preset time interval to obtain second event stream data; The sampling of the first event stream data according to a preset frequency interval or a preset time interval to obtain the second event stream data includes: Extracting a plurality of data features related to a target dimension from the first event stream data according to the preset frequency interval or the preset time interval to obtain a data set consisting of the plurality of data features; wherein the target dimension includes at least one of event, brightness, frequency, position, and phase; generating the second event stream data based on each of the data features in the data set; The step of extracting multiple data features about the target dimension from the first event stream data according to the preset frequency interval or the preset time interval to obtain a data set consisting of the multiple data features includes: Extracting data features about the target dimension from the first event stream data according to the preset frequency interval or the preset time interval, and obtaining multiple data subsets consisting of the data features extracted at each preset time interval; For each of the data subsets, the current data subset is used as the current event stream data, and other data subsets other than the current data subset are used as reference event stream data; Obtaining feature differences between data features of the current event stream data and data features of the reference event stream data, and counting the number of reference event stream data whose feature differences with the current event stream data exceed a preset range; Based on the amount of data, the data subset is selected to form the data set.

2. The distance measurement method according to claim 1, wherein: The analyzing based on the second event stream data to obtain a distance value from the target object to the laser radar includes: Based on the pixel coordinates in the second event stream data and the internal reference parameters of the dynamic vision sensor, the distance value from the target object to the laser radar is obtained.

3. The distance measurement method according to claim 1, wherein: The transmitting module transmits a light beam of at least one modulation frequency, and the preset frequency interval or the preset time interval is related to the value of the modulation frequency.

4. The distance measurement method according to claim 3, characterized in that: In the case where the emission module emits light beams of multiple modulation frequencies, the emission module emits light beams of different modulation frequencies alternately.

5. A laser radar, characterized in that: include: A transmitting module, a dynamic vision sensor and a processing module, wherein the transmitting module and the dynamic vision sensor are both coupled to the processing module; In which, the dynamic vision sensor is used to receive light signals, the transmitting module is used to transmit a light beam to a target object, and the light beam is reflected by the target object to form the light signal, and the processing module is used to execute the ranging method of the laser radar described in any one of claims 1 to 4.

6. A computer-readable storage medium having program instructions stored thereon, characterized in that: When the program instructions are executed by the processor, the laser radar ranging method according to any one of claims 1 to 4 is implemented.

7. A mobile robot, characterized in that: The mobile robot includes a robot body and the laser radar as claimed in claim 5.

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