WO2022061758A1 - 采用点云雷达估计物体速度的方法、点云雷达及系统 - Google Patents
采用点云雷达估计物体速度的方法、点云雷达及系统 Download PDFInfo
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- WO2022061758A1 WO2022061758A1 PCT/CN2020/117879 CN2020117879W WO2022061758A1 WO 2022061758 A1 WO2022061758 A1 WO 2022061758A1 CN 2020117879 W CN2020117879 W CN 2020117879W WO 2022061758 A1 WO2022061758 A1 WO 2022061758A1
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
- G01S13/02—Systems using reflection of radio waves, e.g. primary radar systems; Analogous systems
- G01S13/50—Systems of measurement based on relative movement of target
- G01S13/58—Velocity or trajectory determination systems; Sense-of-movement determination systems
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
- G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
- G01S7/41—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00 using analysis of echo signal for target characterisation; Target signature; Target cross-section
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/23—Clustering techniques
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/20—Analysis of motion
- G06T7/246—Analysis of motion using feature-based methods, e.g. the tracking of corners or segments
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10028—Range image; Depth image; 3D point clouds
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10032—Satellite or aerial image; Remote sensing
- G06T2207/10044—Radar image
Definitions
- the present invention generally relates to the field of radar, and in particular relates to a method, a point cloud radar and a system for estimating the speed of an object by using a point cloud radar.
- Millimeter-wave radar has become one of the important sensors in this field due to its advantages of all-day, all-weather, and low cost.
- the resolution of traditional millimeter-wave radar is limited, so that for some large-sized targets such as vehicles, only a few scattered point targets can be observed, resulting in poor ranging performance compared with lidar.
- the cost of lidar is too high, and it is difficult to meet the vehicle-level certification.
- point cloud radar with large bandwidth and high resolution performance has developed rapidly and has become a hot spot for everyone.
- the point cloud radar observes an increase in the number of scattering points of the same target, but due to the existence of system noise, the measured speed of each scattering point of the target is different, and the true speed of the target cannot be accurately obtained, especially if the target has behaviors such as lane change It is often difficult to accurately obtain the lateral moving speed of the target, which makes it impossible to predict whether the target has the intention of changing lanes, thus affecting the decision-making of the ego vehicle; and the target speed obtained by the radar is the radial speed along the propagation direction of the radar wave, which makes it impossible to pass the measurement. The result gets the exact speed of the target.
- a method, a point cloud radar and a system for estimating the speed of an object using a point cloud radar are required.
- a method for estimating the speed of an object by using point cloud radar comprising: scanning an environment by using a point cloud radar, and obtaining a plurality of scanned objects corresponding to a plurality of sampling points. Motion state parameters of individual object points; clustering a plurality of the sampling points to divide into a plurality of cluster groups, wherein each of the sampling points in the same cluster group is obtained by scanning the same object in the environment ; Estimate the speed of the object according to the motion state parameters of the object points corresponding to the sampling points in the same cluster group.
- a point cloud radar system configured to scan the environment to obtain a plurality of sampling points; coupled with the point cloud radar
- the processing device is used for: acquiring motion state parameters of multiple object points on the object corresponding to the multiple sampling points; clustering the multiple sampling points to divide into multiple clustering groups, wherein the same Each of the sampling points in the clustering group is obtained by scanning the same object in the environment; the speed of the object is estimated according to the motion state parameters of the object points corresponding to the sampling points in the same clustering group.
- a point cloud radar including: a memory for storing a computer program, and a processor coupled with the memory for executing the computer program to implement the following steps : obtain the motion state parameters of multiple object points on the object corresponding to multiple sampling points obtained by scanning the environment through the point cloud radar; cluster the multiple sampling points to divide into multiple clustering groups, Each of the sampling points in the same cluster group is obtained by scanning the same object in the environment; the speed of the object is estimated according to the motion state parameters of the object points corresponding to the sampling points in the same cluster group .
- a computer-readable storage medium including computer-executable instructions, which, when executed by a processor, can perform the above-described method.
- the method, the point cloud radar, the system, and the computer-readable medium for estimating the speed of an object by using a point cloud radar can estimate the speed of the object by using the information of a plurality of sampling points measured by the point cloud radar, and reduce the measurement noise
- the resulting measurement deviation improves the accuracy of speed measurement, improves the radar's measurement performance for scenarios that require extremely accurate speed measurement such as lane changes, and effectively guarantees driving safety.
- FIG. 1 shows a flowchart of steps of a method for estimating the speed of an object by using a point cloud radar according to an embodiment of the present invention
- FIG. 2 shows a schematic structural block diagram of a point cloud radar system according to an embodiment of the present invention
- FIG. 3 shows a schematic top view of a point cloud radar performing scanning measurement according to an embodiment of the present invention
- FIG. 4 shows a schematic diagram of the geometric relationship when deriving the velocity of an object without measuring noise according to an embodiment of the present invention
- FIG. 5 shows an exemplary schematic diagram of a cluster group according to one embodiment of the present invention.
- FIG. 6 shows a schematic structural block diagram of a point cloud radar according to an embodiment of the present invention.
- the present invention provides a method for estimating the speed of an object by using point cloud radar.
- the method includes: scanning the environment through the point cloud radar, and acquiring multiple objects on the object corresponding to multiple sampling points obtained by scanning. motion state parameters of the points; clustering a plurality of the sampling points to divide a plurality of cluster groups, wherein each of the sampling points in the same cluster group is obtained by scanning the same object in the environment; according to The motion state parameters of the object points corresponding to the sampling points in the same cluster group estimate the speed of the object.
- the method for estimating the speed of an object by using the point cloud radar of the present invention can use the information of a plurality of sampling points measured by the point cloud radar to estimate the speed of the object, reduce the measurement deviation caused by the measurement noise, improve the speed measurement accuracy, and improve the The radar's measurement performance for scenes that require extremely accurate speed measurement such as lane change, effectively guarantees driving safety.
- FIG. 1 shows a flowchart of steps of a method 100 for estimating the speed of an object by using a point cloud radar according to an embodiment of the present invention.
- the point cloud radar may be a point cloud millimeter wave radar, a point cloud lidar, or the like.
- the point cloud radar can be mounted on any movable platform, such as any fully autonomous vehicle, semi-autonomous vehicle, drone, etc. with various levels of autonomy (eg, levels 0-5).
- the object may be any other vehicle, pedestrian, bicycle, and various other stationary or moving objects located in the environment where the point cloud radar is located, which is not limited in the present invention.
- the point cloud radar may include any components known in the art, such as power splitters, transmit switches, receive switches, transmit antennas, receive antennas, mixers, control circuits, low noise amplifiers, digital signal processors, etc., for the sake of brevity For the sake of simplicity, the present invention is not described in detail.
- the method 100 may include the following steps:
- Step S110 Scan the environment through the point cloud radar, and acquire motion state parameters of multiple object points on the object corresponding to the multiple sampling points obtained by scanning.
- each sampling point in the plurality of sampling points corresponds to an object point on the scanned object.
- the motion state parameters may include the relative distance, relative angle, relative velocity, etc. between the measured object point and the point cloud radar.
- the relative angle may be the angle between the connection line between the object point and the point cloud radar and the first direction.
- Step S120 Clustering a plurality of sampling points to divide a plurality of clustering groups, wherein each sampling point in the same clustering group is obtained by scanning the same object in the environment.
- any clustering algorithm known in the art may be employed for clustering, such as K-means clustering, mean-shift clustering, DBSCAN clustering, expectation-maximization clustering with GMM, or agglomerative hierarchical clustering.
- Algorithms, etc. are not limited in the present invention.
- Step S130 Estimate the velocity of the object (ie, the true relative velocity) according to the motion state parameters of the object points corresponding to the sampling points in the same cluster group.
- a noisy overdetermined equation can be constructed using the information of these sampling points in the same cluster group, and then the overdetermined equation can be solved by the least squares method, thereby obtaining the velocity of the object.
- a weighted operation may be performed on the motion state parameters of the object points corresponding to each sampling point in the cluster group to obtain the speed of the object.
- the weight coefficient of the weighting operation can be set as required.
- the method 100 may further include: acquiring intensity information of each sampling point, and determining a weighting coefficient of the weighting operation according to the intensity information.
- the intensity information of the sampling point corresponds to the scattering intensity of the radar wave at the corresponding object point on the object.
- the greater the scattering intensity at the object point the greater the intensity of the corresponding sampling point, and the smaller the scattering intensity at the object point.
- the object point with high scattering intensity is a strong scattering point, and its corresponding radar echo signal-to-noise ratio is better, so the sampling point information measured by the radar is more accurate. estimated results.
- the method 100 may further include: using the intensity information p i of each sampling point as a weight coefficient of the motion state parameter of the object point corresponding to the sampling point.
- the method 100 may further include: solving the relative velocity in the first direction according to the measured relative velocity of the object point relative to the point cloud radar and the angle between the connection line between the object point and the point cloud radar and the first direction.
- the first direction may be the left and right directions of the movable platform where the point cloud radar is located and the scanned object
- the second direction may be the front and rear directions of the movable platform where the point cloud radar is located and the scanned object.
- the method 100 may further include: controlling the movement of the movable platform according to the first velocity component and/or the second velocity component.
- the method 100 may further include: determining a first motion condition of the object in front of the movable platform in the first direction according to the first velocity component, and determining, according to the second velocity component, that the movable platform and the object in front of the movable platform are in a second position. a second motion condition in the direction to control the movement of the movable platform according to the first motion condition and the second motion condition.
- the right direction when the right direction is set as the positive direction of the first direction, it can be determined that the left and right distance between the movable platform and the object is increasing according to the positive first velocity component; according to the negative first velocity component, it can be determined that the movable platform and the object are The left and right distance is decreasing; according to the first velocity component being zero, it is determined that the left and right distance between the movable platform and the object remains unchanged.
- the positive direction of the first direction is set to the left, it can be determined that the left-right distance between the movable platform and the object is decreasing according to the positive first velocity component; and the movable platform can be determined to be negative according to the negative first velocity component.
- the left and right distance with the object is increasing; according to the first velocity component being zero, it is determined that the left and right distance between the movable platform and the object remains unchanged.
- the front and rear distance between the movable platform and the object is increasing according to the positive second velocity component; according to the negative second velocity component, it can be determined that the movable platform and the object are The front-to-back distance is decreasing; according to the second velocity component being zero, it is determined that the front-to-back distance between the movable platform and the object remains unchanged.
- the front and rear distance between the movable platform and the object when setting forward as the positive direction of the second direction, it can be determined that the front and rear distance between the movable platform and the object is decreasing according to the second velocity component being positive; according to the negative second velocity component, it can be determined that the movable platform is The front and rear distance with the object is increasing; according to the second velocity component being zero, it is determined that the front and rear distance between the movable platform and the object remains unchanged.
- the method 100 may further include: calculating the relative distance in the first direction according to the measured relative distance between the object point and the point cloud radar and the angle between the connection line between the object point and the point cloud radar and the first direction. and a second relative distance of the relative distance in the second direction to control the movement of the movable platform according to the first relative distance and the second relative distance.
- the movable platform when the first relative distance is small, the left and right distance between the movable platform and the object is small, and the movable platform can be controlled to move an appropriate distance away from the object to avoid collision in the left and right directions; when the second relative distance When it is small, the front and rear distance between the movable platform and the object is small, and the movable platform can be controlled to decelerate appropriately to increase the front and rear distance between the movable platform and the object, so as to avoid collisions in the front and rear directions.
- the method 100 may further include: calculating the theoretical relative velocity of the object point with respect to the point cloud radar according to the estimated speed of the object and the angle between the connection line between the object point and the point cloud radar and the first direction. , and evaluate the performance of point cloud radar based on the theoretical relative velocity.
- the method 100 may further include: calculating a difference between the theoretical relative velocity and the measured relative velocity, and evaluating the performance of the point cloud radar based on the difference. Specifically, if the difference between the theoretical relative speed and the actual relative speed is small, the performance of the point cloud radar is better; if the difference between the theoretical relative speed and the actual relative speed is large, the performance of the point cloud radar is large. If it is poor, an alarm signal can be issued at this time, such as sound, text, graphics and other alarm signals to prompt the user to debug and repair the point cloud radar.
- the method 100 may further include: calculating a ratio of the difference to the measured relative velocity, and evaluating the performance of the point cloud radar based on the ratio. Specifically, if the ratio of the difference to the measured relative velocity is small, the performance of the point cloud radar is good; if the ratio of the difference to the measured relative velocity is large, the performance of the point cloud radar is poor , at this time, an alarm signal, such as sound, text, graphics, etc., can be issued to prompt the user to debug and repair the point cloud radar.
- an alarm signal such as sound, text, graphics, etc.
- the point cloud radar is used to measure multiple sampling points, and the information of the multiple sampling points obtained by the measurement is used to estimate the speed of the object, which reduces the measurement deviation caused by the measurement noise and improves the It improves the speed measurement accuracy, improves the radar's measurement performance for scenarios that require extremely accurate speed measurement such as lane changes, and effectively guarantees driving safety. Further increasing the influence of the weight of strong sampling points makes the obtained estimation result more accurate.
- FIG. 2 shows a schematic structural block diagram of a point cloud radar system 20 according to an embodiment of the present invention.
- the point cloud radar system 20 includes at least a point cloud radar 210 and a processing device 220 coupled to the point cloud radar.
- the point cloud radar 210 and the processing device 220 are shown here as independent devices, those skilled in the art should understand that the processing device 220 may also be integrated into the point cloud radar 210, which is not limited in the present invention.
- the point cloud radar 210 is used to scan the environment to obtain a plurality of sampling points, wherein each sampling point corresponds to an object point on the scanned object.
- the point cloud radar 210 may be a point cloud millimeter-wave radar, a point cloud lidar, or the like.
- point cloud radar 210 may be mounted on any movable platform, such as any fully autonomous vehicle, semi-autonomous vehicle, drone, etc. with various levels of autonomy (eg, levels 0-5).
- the object may be any other vehicle, pedestrian, bicycle, and various other stationary or moving objects located in the environment where the point cloud radar 210 is located, which is not limited in the present invention.
- the point cloud radar 210 may include any components known in the art, such as power splitters, transmit switches, receive switches, transmit antennas, receive antennas, mixers, control circuits, low noise amplifiers, digital signal processors, etc., in order to For the sake of brevity, the present invention is not described in detail.
- FIG. 3 shows a top view when the point cloud radar 210 performs scanning measurement according to one embodiment. It should be understood that although FIG. 3 shows that the object scanned by the point cloud radar 210 is another vehicle, this is only illustrative and not intended to be limiting.
- the point cloud radar 210 observes 1 sampling point on the target vehicle, and the relative distance, relative speed and relative angle between the object point on the target vehicle corresponding to the i-th sampling point obtained by the measurement and the point cloud radar 210 are assumed
- the information is (r i , v i , ⁇ i ), and the true relative velocity of the object point on the target vehicle relative to the point cloud radar is v r .
- the real relative velocity of each object point on it should be the same, so the real relative velocity of the target vehicle relative to the point cloud radar should also be v r .
- the measured relative velocity v i is equal to the projection of the real relative velocity v r along the propagation direction of the radar wave, so we have:
- the unknown variables v x and v y can be solved by combining the measured relative velocity and relative angle information of the two object points, thereby solving the real relative velocity v r of the target vehicle.
- the processing device 220 of the present invention can obtain a more accurate measurement result by performing a series of processing on the information of the sampling point.
- the processing device 220 may acquire motion state parameters of multiple object points on the object corresponding to the multiple sampling points.
- the motion state parameters may include the relative distance, relative angle, relative velocity, etc. of the measured object point and the point cloud radar 210 .
- the relative angle may be the angle between the connection line between the object point and the point cloud radar and the first direction.
- FIG. 5 shows an exemplary schematic diagram of a cluster group according to an embodiment of the present invention.
- any clustering algorithm known in the art may be employed for clustering, such as K-means clustering, mean-shift clustering, DBSCAN clustering, expectation-maximization clustering with GMM, or agglomerative hierarchical clustering.
- Algorithms, etc. are not limited in the present invention.
- the processing device 220 may estimate the velocity of the object (ie, the true relative velocity) according to the motion state parameters of the object points corresponding to the sampling points in the same cluster group.
- the processing device 220 can obtain the information of multiple sampling points. Therefore, a noisy overdetermined equation can be constructed using the information of these sampling points, and then the least squares method is used to solve the overdetermined equation. Determine the equation to get the velocity of the object.
- the processing device 220 may perform a weighted operation on the motion state parameters of the object points corresponding to each sampling point in the cluster group to obtain the speed of the object.
- the weight coefficient of the weighting operation can be set as required.
- the processing device 220 may acquire the intensity information of each sampling point, and determine the weighting coefficient of the weighting operation according to the intensity information.
- the intensity information of the sampling point corresponds to the scattering intensity of the radar wave at the corresponding object point on the object.
- the greater the scattering intensity at the object point the greater the intensity of the corresponding sampling point, and the smaller the scattering intensity at the object point.
- the object point with high scattering intensity is a strong scattering point, and its corresponding radar echo signal-to-noise ratio is better, so the sampling point information measured by the radar is more accurate. estimated results.
- the processing device 220 may use the intensity information p i of each sampling point as the weight coefficient of the motion state parameter of the object point corresponding to the sampling point.
- the processing device 220 may also calculate the relative velocity in the first direction according to the measured relative velocity of the object point relative to the point cloud radar 210 and the angle between the connection line between the object point and the point cloud radar and the first direction.
- the first direction may be the left and right directions of the movable platform where the point cloud radar is located and the scanned object
- the second direction may be the front and rear directions of the movable platform where the point cloud radar is located and the scanned object.
- the processing device 220 may also control the movement of the movable platform according to the first velocity component and/or the second velocity component.
- the processing device 220 may determine a first motion condition of the object in front of the movable platform in the first direction according to the first velocity component, and determine the movable platform and the object in front of it in the second direction according to the second velocity component. the second motion condition, so as to control the movement of the movable platform according to the first motion condition and the second motion condition.
- the processing device 220 may determine that the left-right distance between the movable platform and the object is increasing according to the positive first velocity component; determine that the movable platform is moving according to the negative first velocity component. The left and right distance between the platform and the object is decreasing; according to the first velocity component being zero, it is determined that the left and right distance between the movable platform and the object remains unchanged.
- the processing device 220 may determine that the left-right distance between the movable platform and the object is decreasing according to the positive first velocity component; The left and right distance between the movable platform and the object is increasing; according to the first velocity component being zero, it is determined that the left and right distance between the movable platform and the object remains unchanged.
- the processing device 220 may determine that the distance between the movable platform and the object is increasing according to the second velocity component being positive; and determine that the movable platform is moving according to the negative second velocity component. The front and rear distance between the platform and the object is decreasing; according to the second velocity component being zero, it is determined that the front and rear distance between the movable platform and the object remains unchanged.
- the processing device 220 may determine that the distance between the movable platform and the object is decreasing according to the second velocity component being positive; determine that the distance between the movable platform and the object is decreasing according to the second velocity component being negative; The front and rear distance between the movable platform and the object is increasing; according to the second velocity component being zero, it is determined that the front and rear distance between the movable platform and the object remains unchanged.
- the processing device 220 may also calculate the relative distance between the object point and the point cloud radar 210 according to the measured relative distance and the angle between the connection line between the object point and the point cloud radar 210 and the first direction. A first relative distance upward and a second relative distance of the relative distance in the second direction to control the movement of the movable platform according to the first relative distance and the second relative distance.
- the processing device 220 can control the movable platform to move an appropriate distance away from the object to avoid collision in the left and right directions;
- the processing device 220 can control the movable platform to decelerate appropriately to increase the front and rear distance between the movable platform and the object, so as to avoid collision in the front and rear directions.
- the processing device 220 may also calculate the theoretical relative relationship between the object point and the point cloud radar 210 according to the estimated speed of the object and the angle between the connection line between the object point and the point cloud radar 210 and the first direction. speed and evaluate the performance of the point cloud radar 210 based on this theoretical relative speed.
- the processing device 220 may also calculate the difference between the theoretical relative velocity and the measured relative velocity, and evaluate the performance of the point cloud radar 210 based on the difference. Specifically, if the difference between the theoretical relative velocity and the actual relative velocity is small, the performance of the point cloud radar 210 is good; if the difference between the theoretical relative velocity and the actual relative velocity is large, the point cloud radar 210 The performance of the point cloud radar 210 is poor, and the control device 120 may issue an alarm signal, such as sound, text, graphics, etc., to prompt the user to debug and repair the point cloud radar 210 .
- an alarm signal such as sound, text, graphics, etc.
- the processing device 220 may also calculate a ratio of the difference to the measured relative velocity, and evaluate the performance of the point cloud radar 210 based on the ratio. Specifically, if the ratio of the difference to the measured relative velocity is small, the performance of the point cloud radar 210 is good; if the ratio of the difference to the measured relative velocity is large, the performance of the point cloud radar 210 is high In this case, the control device 120 can send out an alarm signal, such as sound, text, graphics, etc., to prompt the user to debug and repair the point cloud radar 210 .
- an alarm signal such as sound, text, graphics, etc.
- the point cloud radar system is adopted, the point cloud radar is used to measure multiple sampling points, and the information of the multiple sampling points obtained by the measurement is used to estimate the speed of the object, which reduces the measurement deviation caused by the measurement noise and improves the speed measurement accuracy. , which improves the radar's measurement performance for scenarios that require extremely accurate speed measurement such as lane changes, and effectively guarantees driving safety. Further increasing the influence of the weight of strong sampling points makes the obtained estimation result more accurate.
- FIG. 6 shows a schematic structural block diagram of a point cloud radar 60 according to an embodiment of the present invention.
- the point cloud radar 60 may be a point cloud millimeter-wave radar, a point cloud lidar, or the like.
- point cloud radar 60 may be mounted on any movable platform, such as any fully autonomous vehicle, semi-autonomous vehicle, drone, etc. with various levels of autonomous driving (eg, levels 0-5).
- the object may be any other vehicle, pedestrian, bicycle, and various other stationary or moving objects located in the environment where the point cloud radar 60 is located, which is not limited in the present invention.
- point cloud radar 60 includes at least a memory 610 and a processor 620 coupled to the memory. It should be understood that point cloud radar 60 may also include any components known in the art, such as power splitters, transmit switches, receive switches, transmit antennas, receive antennas, mixers, control circuits, low noise amplifiers, digital signal processors, etc. , for the sake of brevity, the present invention is not described in detail.
- a computer program is stored in the memory 610, and the computer program can be executed by the processor 620 to realize the following steps:
- Step S1 Obtain motion state parameters of multiple object points on the object corresponding to multiple sampling points obtained by scanning the environment with the point cloud radar.
- each sampling point in the plurality of sampling points corresponds to an object point on the scanned object.
- the motion state parameters may include the relative distance, relative angle, relative velocity, etc. of the measured object point and the point cloud radar 60 .
- the relative angle may be the angle between the connection line between the object point and the point cloud radar and the first direction.
- Step S2 Clustering a plurality of sampling points to divide a plurality of clustering groups, wherein each sampling point in the same clustering group is obtained by scanning the same object in the environment.
- any clustering algorithm known in the art may be employed for clustering, such as K-means clustering, mean-shift clustering, DBSCAN clustering, expectation-maximization clustering with GMM, or agglomerative hierarchical clustering.
- Algorithms, etc. are not limited in the present invention.
- Step S3 Estimate the velocity of the object (ie, the true relative velocity) according to the motion state parameters of the object points corresponding to the sampling points in the same cluster group.
- a noisy overdetermined equation can be constructed using the information of these sampling points in the same cluster group, and then the overdetermined equation can be solved by the least squares method, thereby obtaining the velocity of the object.
- a weighted operation may be performed on the motion state parameters of the object points corresponding to each sampling point in the cluster group to obtain the speed of the object.
- the weight coefficient of the weighting operation can be set as required.
- the method may further include the step of: acquiring intensity information of each sampling point, and determining a weight coefficient of the weighting operation according to the intensity information.
- the intensity information of the sampling point corresponds to the scattering intensity of the radar wave at the corresponding object point on the object.
- the greater the scattering intensity at the object point the greater the intensity of the corresponding sampling point, and the smaller the scattering intensity at the object point.
- the object point with high scattering intensity is a strong scattering point, and its corresponding radar echo signal-to-noise ratio is better, so the sampling point information measured by the radar is more accurate. estimated results.
- the intensity information p i of each sampling point may be used as the weight coefficient of the motion state parameter of the object point corresponding to the sampling point.
- the relative velocity of the object point relative to the point cloud radar 60 and the angle between the connection line between the object point and the point cloud radar 60 and the first direction can also be used to calculate the relative velocity in the first direction.
- the first direction may be the left and right directions of the movable platform where the point cloud radar is located and the scanned object
- the second direction may be the front and rear directions of the movable platform where the point cloud radar is located and the scanned object.
- the movement of the movable platform can also be controlled according to the first velocity component and/or the second velocity component.
- the first motion condition of the object in front of the movable platform in the first direction can be determined according to the first velocity component
- the second movement of the movable platform and the object in front of it in the second direction can be determined according to the second velocity component.
- the right direction when the right direction is set as the positive direction of the first direction, it can be determined that the left and right distance between the movable platform and the object is increasing according to the positive first velocity component; according to the negative first velocity component, it can be determined that the movable platform and the object are The left and right distance is decreasing; according to the first velocity component being zero, it is determined that the left and right distance between the movable platform and the object remains unchanged.
- the positive direction of the first direction is set to the left, it can be determined that the left-right distance between the movable platform and the object is decreasing according to the positive first velocity component; and the movable platform can be determined to be negative according to the negative first velocity component.
- the left and right distance with the object is increasing; according to the first velocity component being zero, it is determined that the left and right distance between the movable platform and the object remains unchanged.
- the front and rear distance between the movable platform and the object is increasing according to the positive second velocity component; according to the negative second velocity component, it can be determined that the movable platform and the object are The front-to-back distance is decreasing; according to the second velocity component being zero, it is determined that the front-to-back distance between the movable platform and the object remains unchanged.
- the front and rear distance between the movable platform and the object when setting forward as the positive direction of the second direction, it can be determined that the front and rear distance between the movable platform and the object is decreasing according to the second velocity component being positive; according to the negative second velocity component, it can be determined that the movable platform is The front and rear distance with the object is increasing; according to the second velocity component being zero, it is determined that the front and rear distance between the movable platform and the object remains unchanged.
- the relative distance between the object point and the point cloud radar 60 and the angle between the connecting line between the object point and the point cloud radar 60 and the first direction can also be used to calculate the relative distance in the first direction.
- a relative distance and a second relative distance of the relative distance in the second direction to control the movement of the movable platform according to the first relative distance and the second relative distance.
- the movable platform when the first relative distance is small, the left and right distance between the movable platform and the object is small, and the movable platform can be controlled to move an appropriate distance away from the object to avoid collision in the left and right directions; when the second relative distance When it is small, the front and rear distance between the movable platform and the object is small, and the movable platform can be controlled to decelerate appropriately to increase the front and rear distance between the movable platform and the object, so as to avoid collisions in the front and rear directions.
- the theoretical relative velocity of the object point relative to the point cloud radar 60 can also be calculated according to the estimated speed of the object and the angle between the connection line between the object point and the point cloud radar 60 and the first direction, and The performance of the point cloud radar 60 is evaluated based on this theoretical relative velocity.
- the difference between the theoretical relative velocity and the measured relative velocity can also be calculated, and the performance of the point cloud radar 60 can be evaluated based on the difference. Specifically, if the difference between the theoretical relative velocity and the actual relative velocity is small, the performance of the point cloud radar 60 is better; if the difference between the theoretical relative velocity and the actual relative velocity is large, the point cloud radar 60 The performance of the point cloud radar 60 is poor, and an alarm signal, such as sound, text, graphics, etc., can be issued at this time to prompt the user to debug and repair the point cloud radar 60 .
- an alarm signal such as sound, text, graphics, etc.
- a ratio of the difference to the measured relative velocity can also be calculated, and the performance of the point cloud radar 60 can be evaluated based on the ratio. Specifically, if the ratio of the difference to the measured relative velocity is small, the performance of the point cloud radar 60 is good; if the ratio of the difference to the measured relative velocity is large, the performance of the point cloud radar 60 is high If it is poor, an alarm signal, such as sound, text, graphics, etc., can be issued at this time to prompt the user to debug and repair the point cloud radar 60 .
- the point cloud radar of this embodiment measures multiple sampling points, and uses the information of the multiple sampling points obtained by the measurement to estimate the speed of the object, which reduces the measurement deviation caused by the measurement noise, improves the speed measurement accuracy, and improves the radar's anti-variation performance.
- This embodiment provides a computer-readable medium on which a computer program is stored, the computer program executes the above-mentioned method for estimating the speed of an object by using a point cloud radar when running.
- the speed of the object when the computer program thereon is executed, the speed of the object can be estimated by using the information of a plurality of sampling points measured by the point cloud radar, the measurement deviation caused by the measurement noise is reduced, and the improvement is improved.
- the speed measurement accuracy improves the radar's measurement performance for scenarios that require extremely accurate speed measurement, such as lane changes, and effectively guarantees driving safety. Further increasing the influence of the weight of strong sampling points makes the obtained estimation result more accurate.
- the disclosed apparatus and method may be implemented in other manners.
- the device embodiments described above are only illustrative.
- the division of the units is only a logical function division. In actual implementation, there may be other division methods.
- multiple units or components may be combined or May be integrated into another device, or some features may be omitted, or not implemented.
- Various component embodiments of the present invention may be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof.
- a microprocessor or a digital signal processor (DSP) may be used in practice to implement some or all functions of some modules in the article analysis device according to the embodiment of the present invention.
- DSP digital signal processor
- the present invention may also be implemented as apparatus programs (eg, computer programs and computer program products) for performing part or all of the methods described herein.
- Such a program implementing the present invention may be stored on a computer-readable medium, or may be in the form of one or more signals. Such signals may be downloaded from Internet sites, or provided on carrier signals, or in any other form.
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Abstract
采用点云雷达(210,60)估计物体速度的方法、点云雷达(210,60)及系统(20),方法包括:通过点云雷达(210,60)对环境进行扫描,获取扫描得到的多个采样点对应的物体上的多个物点的运动状态参数(S110);对多个采样点进行聚类,以划分出多个聚类组,其中同一聚类组内的各个采样点是扫描环境中同一物体得到的(S120);根据同一聚类组内的采样点对应的物点的运动状态参数估计物体的速度(S130)。由此,能够利用由点云雷达(210,60)测量得到的多个采样点信息估计物体的速度,降低了测量噪声带来的测量偏差,提升了速度测量精度,改善了雷达对变道等速度测量要求极为准确的场景的测量性能,有力地保障了行车安全性。
Description
本发明总地涉及雷达领域,具体而言涉及一种采用点云雷达估计物体速度的方法、点云雷达及系统。
近年来自动驾驶及辅助驾驶系统发展非常迅速,毫米波雷达因其全天时、全天候、成本低等优点,已成为该领域重要的传感器之一。但是传统毫米波雷达的分辨率有限,导致对于一些较大尺寸的目标如车辆等,仅能观测到极少的散射点目标,导致测距性能要差于激光雷达。但是激光雷达成本太高,且难以符合车规级认证。鉴于此,具备大带宽、高分辨性能的点云雷达迅速发展起来,成为大家追捧的热点。
点云雷达观测到同一个目标的散射点数目增多,但由于系统噪声的存在,测得的目标的各个散射点的速度存在差异,无法准确获取目标的真实速度,特别是目标存在变道等行为时,往往难以准确获取目标的横向移动速度,导致无法预测目标是否具有变道意向,从而影响自我车辆的决策;且雷达获取的目标速度为沿雷达波传播方向的径向速度,导致无法通过测量结果获取目标的准确速度。
为了解决这个问题,需要一种采用点云雷达估计物体速度的方法、点云雷达及系统。
发明内容
在发明内容部分中引入了一系列简化形式的概念,这将在具体实施方式部分中进一步详细说明。本发明的发明内容部分并不意味着要试图限定出所要求保护的技术方案的关键特征和必要技术特征,更不意味着试图确定所要求保护的技术方案的保护范围。
鉴于上述技术问题的存在,有必要提出一种采用点云雷达估计物体速度的方法、点云雷达及系统和计算机可读存储介质,以解决现有 的采用点云雷达准确估计物体速度的问题。
根据本发明实施例的一方面,提供了一种采用点云雷达估计物体速度的方法,该方法包括:通过点云雷达对环境进行扫描,获取扫描得到的多个采样点对应的物体上的多个物点的运动状态参数;对多个所述采样点进行聚类,以划分出多个聚类组,其中同一聚类组内的各个所述采样点是扫描所述环境中同一物体得到的;根据所述同一聚类组内的所述采样点对应的物点的运动状态参数估计所述物体的速度。
根据本发明实施例的另一方面提供了一种点云雷达系统,该点云雷达系统包括:点云雷达,用于对环境进行扫描以得到多个采样点;与所述点云雷达耦连的处理装置,用于:获取所述多个采样点对应的物体上的多个物点的运动状态参数;对所述多个采样点进行聚类,以划分出多个聚类组,其中同一聚类组内的各个所述采样点是扫描所述环境中同一物体得到的;根据所述同一聚类组内的所述采样点对应的物点的运动状态参数估计所述物体的速度。
根据本发明实施例的又一方面提供了一种点云雷达,包括:存储器,用于存储计算机程序,和与所述存储器耦连的处理器,用于执行所述计算机程序,以实现以下步骤:获取通过点云雷达对环境进行扫描得到的多个采样点对应的物体上的多个物点的运动状态参数;对多个所述采样点进行聚类,以划分出多个聚类组,其中同一聚类组内的各个所述采样点是扫描所述环境中同一物体得到的;根据所述同一聚类组内的所述采样点对应的物点的运动状态参数估计所述物体的速度。
根据本发明实施例的再一方面提供了一种计算机可读存储介质,包括计算机可执行指令,所述计算机可执行指令在由处理器执行时,能够执行如上所述的方法。
本发明的实施例的采用点云雷达估计物体速度的方法、点云雷达及系统和计算机可读介质,能够利用由点云雷达测量得到的多个采样点信息估计物体的速度,降低了测量噪声带来的测量偏差,提升了速度测量精度,改善了雷达对变道等速度测量要求极为准确的场景的测量性能,有力地保障了行车安全性。
本发明的下列附图在此作为本发明的一部分用于理解本发明。附图中示出了本发明的实施例及其描述,用来解释本发明的原理。
附图中:
图1示出了根据本发明的一个实施例的采用点云雷达估计物体速度的方法的步骤流程图;
图2示出了根据本发明的一个实施例的点云雷达系统的示意性结构框图;
图3示出了根据本发明的一个实施例的点云雷达进行扫描测量时的示意性俯视图;
图4示出了根据本发明的一个实施例的在没有测量噪声情况下推导物体速度时的几何关系示意图;
图5示出了根据本发明的一个实施例的聚类组的示例性示意图。
图6示出了根据本发明的一个实施例的点云雷达的示意性结构框图。
为了使得本发明的目的、技术方案和优点更为明显,下面将参照附图详细描述根据本发明的示例实施例。显然,所描述的实施例仅仅是本发明的一部分实施例,而不是本发明的全部实施例,应理解,本发明不受这里描述的示例实施例的限制。基于本发明中描述的本发明实施例,本领域技术人员在没有付出创造性劳动的情况下所得到的所有其它实施例都应落入本发明的保护范围之内。
为了解决上述技术问题,本发明提供一种采用点云雷达估计物体速度的方法,该方法包括:通过点云雷达对环境进行扫描,获取扫描得到的多个采样点对应的物体上的多个物点的运动状态参数;对多个所述采样点进行聚类,以划分出多个聚类组,其中同一聚类组内的各个所述采样点是扫描所述环境中同一物体得到的;根据所述同一聚类组内的所述采样点对应的物点的运动状态参数估计所述物体的速度。
本发明的采用点云雷达估计物体速度的方法,能够利用由点云雷达测量得到的多个采样点信息估计物体的速度,降低了测量噪声带来 的测量偏差,提升了速度测量精度,改善了雷达对变道等速度测量要求极为准确的场景的测量性能,有力地保障了行车安全性。
下面参考具体实施例详细描述根据本发明的实施例的采用点云雷达估计物体速度的方法、点云雷达及系统和计算机可读介质。
根据一个实施例,提供了一种采用点云雷达估计物体速度的方法。参考图1,图1示出了根据本发明的一个实施例的采用点云雷达估计物体速度的方法100的步骤流程图。
示例性地,点云雷达可以为点云毫米波雷达、点云激光雷达等。示例性地,点云雷达可以装载于任何可移动平台上,例如具有各种自动驾驶级别(例如,0-5级)的任何全自动驾驶车辆、半自动驾驶车辆,无人机等。示例性地,物体可以为位于点云雷达所处环境中的任何其他车辆、行人、自行车、以及各种其他静止或运动的物体等,本发明对此不作限定。
其中,点云雷达可以包括任何本领域公知的元件,例如,功率分离器、发射开关、接收开关、发射天线、接收天线、混合器、控制电路、低噪声放大器、数字信号处理器等,为了简洁起见,本发明并未详细介绍。
如图1所示,方法100可以包括如下步骤:
步骤S110:通过点云雷达对环境进行扫描,获取扫描得到的多个采样点对应的物体上的多个物点的运动状态参数。
其中,多个采样点中的每个采样点均对应于扫描到的物体上的一个物点。
示例性地,运动状态参数可以包括测量得到的物点与点云雷达的相对距离、相对角度、相对速度等。其中,相对角度可以为物点与点云雷达的连线和第一方向的夹角。
步骤S120:对多个采样点进行聚类,以划分出多个聚类组,其中同一聚类组内的各个采样点是扫描所述环境中同一物体得到的。
示例性地,可以采用本领域公知的任何聚类算法进行聚类,例如K均值聚类算法、均值偏移聚类算法、DBSCAN聚类算法、用GMM的最大期望聚类算法或凝聚层次聚类算法等,本发明对此不作限定。
步骤S130:根据同一聚类组内的采样点对应的物点的运动状态参数估计物体的速度(即真实相对速度)。
具体地,可以利用同一聚类组内的这些采样点的信息构造一个有噪声的超定方程,然后利用最小二乘法求解该超定方程,从而得到物体的速度。
下面阐述一下利用最小二乘法估计v
x和v
y的求解过程。
根据物点的运动状态参数可以构造如下方程:
v
e=Av
r
其中,
v
e=[v
1 v
2 … v
I]
T
v
r=[v
x v
y]
T
根据以上方程,利用最小二乘法可以求得下列等式:
v
r=(A
TA)
-1A
Tv
e (1)
示例性地,可以对该聚类组内的各个采样点对应的物点的运动状态参数进行加权运算以得到物体的速度。
其中,加权运算的权重系数可以根据需要进行设置。示例性地,方法100还可以包括:获取各个采样点的强度信息,根据该强度信息确定加权运算的权重系数。其中,采样点的强度信息对应于雷达波在物体上相应物点处的散射强度,在物点处的散射强度越大,相应的采样点的强度越大,在物点处的散射强度越小,相应的采样点的强度越小。散射强度大的物点为强散射点,其对应的雷达回波信噪比较好,从而雷达测得的采样点信息更为准确,因此增加强散射点的权重影响,可以获得更为准确的估计结果。
其中,根据采样点的强度信息P确定加权运算的权重系数时,上述等式(1)相应地变换为:
v
r=(A
TPA)
-1A
TPv
e
示例性地,方法100还可以包括:将每一采样点的强度信息p
i,作为该采样点对应的物点的运动状态参数的权重系数。
示例性地,方法100还可以包括:根据测量得到的物点相对于点云雷达的相对速度以及物点与点云雷达的连线和第一方向的夹角求解该相对速度在该第一方向上的第一速度分量和第二方向上的第二速度分量,其中第二方向与第一方向垂直。示例性地,第一方向可以为点云雷达所在的可移动平台与扫描到的物体的左右方向,第二方向可以为点云雷达所在的可移动平台与扫描到的物体的前后方向。
示例性地,方法100还可以包括:根据该第一速度分量和/或第二速度分量控制可移动平台的运动。
示例性地,方法100还可以包括:根据第一速度分量确定可移动平台前方的物体在第一方向上的第一运动状况,以及根据第二速度分量确定可移动平台与其前方的物体在第二方向上的第二运动状况,以根据该第一运动状况和第二运动状况控制可移动平台的运动。
例如,当设置向右为第一方向的正方向时,可以根据第一速度分量为正,确定可移动平台与物体的左右距离在增加;根据第一速度分量为负,确定可移动平台与物体的左右距离在减小;根据第一速度分量为零,确定可移动平台与物体的左右距离保持不变。又例如,当设置向左为第一方向的正方向时,可以根据第一速度分量为正,确定可移动平台与物体的左右距离在减小;根据第一速度分量为负,确定可移动平台与物体的左右距离在增加;根据第一速度分量为零,确定可移动平台与物体的左右距离保持不变。
例如,当设置向后为第二方向的正方向时,可以根据第二速度分量为正,确定可移动平台与物体的前后距离在增加;根据第二速度分量为负,确定可移动平台与物体的前后距离在减小;根据第二速度分量为零,确定可移动平台与物体的前后距离保持不变。又例如,当设置向前为第二方向的正方向时,可以根据第二速度分量为正,确定可移动平台与物体的前后距离在减小;根据第二速度分量为负,确定可移动平台与物体的前后距离在增加;根据第二速度分量为零,确定可移动平台与物体的前后距离保持不变。
示例性地,方法100还可以包括:根据测量得到的物点与点云雷达的相对距离以及物点与点云雷达的连线和第一方向的夹角计算出该相对距离在第一方向上的第一相对距离和该相对距离在第二方向上的第二相对距离,以根据该第一相对距离和第二相对距离控制可移动平台的运动。
例如,当第一相对距离较小时,即可移动平台向与物体的左右距离较小,可以控制可移动平台向远离物体的方向运动适当距离,以避免左右方向上发生碰撞;当第二相对距离较小时,即可移动平台向与物体的前后距离较小,可以控制可移动平台适当减速,加大其与物体的前后距离,从而避免前后方向上发生碰撞。
示例性地,方法100还可以包括:根据估计出的物体的速度以及某物点与点云雷达的连线和第一方向的夹角,计算出该物点相对于点云雷达的理论相对速度,并基于该理论相对速度评估点云雷达的性能。
示例性地,方法100还可以包括:计算出该理论相对速度与测得的相对速度的差值,并基于该差值评估点云雷达的性能。具体地,如果该理论相对速度与实际的相对速度的差值较小,则点云雷达的性能较好;如果该理论相对速度与实际的相对速度的差值较大,则点云雷达的性能较差,此时可以发出报警信号,例如声音、文字、图形等报警信号,以提示用户对点云雷达进行调试、修理等。
示例性地,方法100还可以包括:计算出该差值与测得的相对速度的比值,并基于该比值评估点云雷达的性能。具体地,如果该差值与测得的相对速度的比值较小,则点云雷达的性能较好;如果该差值与测得的相对速度的比值较大,则点云雷达的性能较差,此时可以发出报警信号,例如声音、文字、图形等报警信号,以提示用户对点云雷达进行调试、修理等。
本实施例的采用点云雷达估计物体速度的方法,利用点云雷达测量多个采样点,并利用测量得到的多个采样点信息估计物体的速度,降低了测量噪声带来的测量偏差,提升了速度测量精度,改善了雷达对变道等速度测量要求极为准确的场景的测量性能,有力地保障了行车安全性。进一步地增加强采样点的权重影响,使得获得的估计结果更为准确。
根据另一实施例,提供了一种点云雷达系统。参考图2,图2示出了根据本发明的一个实施例的点云雷达系统20的示意性结构框图。在一个实施例中,点云雷达系统20至少包括点云雷达210和与该点云雷达耦连的处理装置220。虽然此处示出了点云雷达210与处理装置220为独立的装置,但本领域技术人员应理解,处理装置220还可以集成到点云雷达210中,本发明对此不作限定。
其中,点云雷达210用于对环境进行扫描以得到多个采样点,其中每个采样点均对应于扫描到的物体上的一个物点。示例性地,点云雷达210可以为点云毫米波雷达、点云激光雷达等。示例性地,点云雷达210可以装载于任何可移动平台上,例如具有各种自动驾驶级别(例如,0-5级)的任何全自动驾驶车辆、半自动驾驶车辆,无人机等。示例性地,物体可以为位于点云雷达210所处环境中的任何其他车辆、行人、自行车、以及各种其他静止或运动的物体等,本发明对此不作限定。
其中,点云雷达210可以包括任何本领域公知的元件,例如,功率分离器、发射开关、接收开关、发射天线、接收天线、混合器、控制电路、低噪声放大器、数字信号处理器等,为了简洁起见,本发明并未详细介绍。
参考图3,图3示出了根据一个实施例的点云雷达210进行扫描测量时的俯视图。应理解,虽然图3中示出了点云雷达210扫描到的物体为另一车辆,但这仅仅是示意性的,并不意图是限制。
其中,假设点云雷达210观测到目标车辆上的I个采样点,设其测量得到的第i个采样点对应的目标车辆上的物点与点云雷达210的相对距离、相对速度及相对角度的信息为(r
i,v
i,θ
i),目标车辆上的该物点相对于点云雷达的真实相对速度为v
r。对于一个刚体,例如车辆等,其上各个物点的真实相对速度应该是相同的,因此目标车辆相对于点云雷达的真实相对速度也应为v
r。
在没有测量噪声情况下,根据如图4所示的几何关系示意图,可推导得到如下关系:
假设目标车辆相对于点云雷达的真实相对速度v
r沿x轴和y轴的 分量分别为v
x和v
y,则有:
而测得的相对速度v
i等于真实相对速度v
r沿雷达波传播方向的投影,因此有:
v
i=v
xcosθ
i+v
ysinθ
i
在没有测量噪声的情况下,联合测得的两个物点的相对速度及相对角度的信息,即可求解出未知变量v
x和v
y,从而求解出目标车辆的真实相对速度v
r。
但是,实际测量过程中不可避免地会出现测量噪声,导致无法通过物体上两个物点的信息估计出准确的物体的速度信息。
而本发明的处理装置220通过对采样点的信息进行一系列处理,可以获得更加准确的测量结果。
具体地,处理装置220可以获取多个采样点对应的物体上的多个物点的运动状态参数。示例性地,运动状态参数可以包括测量得到的物点与点云雷达210的相对距离、相对角度、相对速度等。其中,相对角度可以为物点与点云雷达的连线和第一方向的夹角。
然后,处理装置220可以对该多个采样点进行聚类,以划分出多个聚类组,其中同一聚类组内的各个采样点是扫描环境中同一物体得到的。参见图5,图5示出了根据本发明的一个实施例的聚类组的示例性示意图。
示例性地,可以采用本领域公知的任何聚类算法进行聚类,例如K均值聚类算法、均值偏移聚类算法、DBSCAN聚类算法、用GMM的最大期望聚类算法或凝聚层次聚类算法等,本发明对此不作限定。
然后,处理装置220可以根据同一聚类组内的采样点对应的物点的运动状态参数估计物体的速度(即真实相对速度)。
由于点云雷达210获得的是多个采样点,处理装置220可以获取多个采样点的信息,因此可以利用这些采样点的信息构造一个有噪声的超定方程,然后利用最小二乘法求解该超定方程,从而得到物体的速度。
下面阐述一下利用最小二乘方法估计v
x和v
y的求解过程。
根据物点的运动状态参数可以构造如下方程:
v
e=Av
r
其中,
v
e=[v
1 v
2 … v
I]
T
v
r=[v
x v
y]
T
根据以上方程,利用最小二乘法可以求得下列等式:
v
r=(A
TA)
-1A
Tv
e (1)
示例性地,处理装置220可以对该聚类组内的各个采样点对应的物点的运动状态参数进行加权运算以得到物体的速度。
其中,加权运算的权重系数可以根据需要进行设置。示例性地,处理装置220可以获取各个采样点的强度信息,根据该强度信息确定加权运算的权重系数。其中,采样点的强度信息对应于雷达波在物体上相应物点处的散射强度,在物点处的散射强度越大,相应的采样点的强度越大,在物点处的散射强度越小,相应的采样点的强度越小。散射强度大的物点为强散射点,其对应的雷达回波信噪比较好,从而雷达测得的采样点信息更为准确,因此增加强散射点的权重影响,可以获得更为准确的估计结果。
其中,根据采样点的强度信息P确定加权运算的权重系数时,上述等式(1)相应地变换为:
v
r=(A
TPA)
-1A
TPv
e
示例性地,处理装置220可以将每一采样点的强度信息p
i,作为该采样点对应的物点的运动状态参数的权重系数。
示例性地,处理装置220还可以根据测量得到的物点相对于点云雷达210的相对速度以及物点与点云雷达的连线和第一方向的夹角求解该相对速度在该第一方向上的第一速度分量和第二方向上的第 二速度分量,其中第二方向与第一方向垂直。示例性地,第一方向可以为点云雷达所在的可移动平台与扫描到的物体的左右方向,第二方向可以为点云雷达所在的可移动平台与扫描到的物体的前后方向。
示例性地,处理装置220还可以根据该第一速度分量和/或第二速度分量控制可移动平台的运动。
示例性地,处理装置220可以根据第一速度分量确定可移动平台前方的物体在第一方向上的第一运动状况,以及根据第二速度分量确定可移动平台与其前方的物体在第二方向上的第二运动状况,以根据该第一运动状况和第二运动状况控制可移动平台的运动。
例如,当设置向右为第一方向的正方向时,处理装置220可以根据第一速度分量为正,确定可移动平台与物体的左右距离在增加;根据第一速度分量为负,确定可移动平台与物体的左右距离在减小;根据第一速度分量为零,确定可移动平台与物体的左右距离保持不变。又例如,当设置向左为第一方向的正方向时,处理装置220可以根据第一速度分量为正,确定可移动平台与物体的左右距离在减小;根据第一速度分量为负,确定可移动平台与物体的左右距离在增加;根据第一速度分量为零,确定可移动平台与物体的左右距离保持不变。
例如,当设置向后为第二方向的正方向时,处理装置220可以根据第二速度分量为正,确定可移动平台与物体的前后距离在增加;根据第二速度分量为负,确定可移动平台与物体的前后距离在减小;根据第二速度分量为零,确定可移动平台与物体的前后距离保持不变。又例如,当设置向前为第二方向的正方向时,处理装置220可以根据第二速度分量为正,确定可移动平台与物体的前后距离在减小;根据第二速度分量为负,确定可移动平台与物体的前后距离在增加;根据第二速度分量为零,确定可移动平台与物体的前后距离保持不变。
示例性地,处理装置220还可以根据测量得到的物点与点云雷达210的相对距离以及物点与点云雷达210的连线和第一方向的夹角计算出该相对距离在第一方向上的第一相对距离和该相对距离在第二方向上的第二相对距离,以根据该第一相对距离和第二相对距离控制可移动平台的运动。
例如,当第一相对距离较小时,即可移动平台向与物体的左右距 离较小,处理装置220可以控制可移动平台向远离物体的方向运动适当距离,以避免左右方向上发生碰撞;当第二相对距离较小时,即可移动平台向与物体的前后距离较小,处理装置220可以控制可移动平台适当减速,加大其与物体的前后距离,从而避免前后方向上发生碰撞。
示例性地,处理装置220还可以根据估计出的物体的速度以及某物点与点云雷达210的连线和第一方向的夹角,计算出该物点相对于点云雷达210的理论相对速度,并基于该理论相对速度评估点云雷达210的性能。
示例性地,处理装置220还可以计算出该理论相对速度与测得的相对速度的差值,并基于该差值评估点云雷达210的性能。具体地,如果该理论相对速度与实际的相对速度的差值较小,则点云雷达210的性能较好;如果该理论相对速度与实际的相对速度的差值较大,则点云雷达210的性能较差,此时控制装置120可以发出报警信号,例如声音、文字、图形等报警信号,以提示用户对点云雷达210进行调试、修理等。
示例性地,处理装置220还可以计算出该差值与测得的相对速度的比值,并基于该比值评估点云雷达210的性能。具体地,如果该差值与测得的相对速度的比值较小,则点云雷达210的性能较好;如果该差值与测得的相对速度的比值较大,则点云雷达210的性能较差,此时控制装置120可以发出报警信号,例如声音、文字、图形等报警信号,以提示用户对点云雷达210进行调试、修理等。
本实施例的采用点云雷达系统,利用点云雷达测量多个采样点,并利用测量得到的多个采样点信息估计物体的速度,降低了测量噪声带来的测量偏差,提升了速度测量精度,改善了雷达对变道等速度测量要求极为准确的场景的测量性能,有力地保障了行车安全性。进一步地增加强采样点的权重影响,使得获得的估计结果更为准确。
根据又一实施例,提供一种点云雷达。参考图6,图6示出了根据本发明的一个实施例的点云雷达60的示意性结构框图。示例性地,点云雷达60可以为点云毫米波雷达、点云激光雷达等。示例性地, 点云雷达60可以装载于任何可移动平台上,例如具有各种自动驾驶级别(例如,0-5级)的任何全自动驾驶车辆、半自动驾驶车辆,无人机等。示例性地,物体可以为位于点云雷达60所处环境中的任何其他车辆、行人、自行车、以及各种其他静止或运动的物体等,本发明对此不作限定。
在一个实施例中,点云雷达60至少包括存储器610和与该存储器耦连的处理器620。应理解,点云雷达60还可以包括任何本领域公知的元件,例如,功率分离器、发射开关、接收开关、发射天线、接收天线、混合器、控制电路、低噪声放大器、数字信号处理器等,为了简洁起见,本发明并未详细介绍。
其中,存储器610中存储有计算机程序,该计算机程序可以由处理器620执行,以实现如下步骤:
步骤S1:获取通过点云雷达对环境进行扫描得到的多个采样点对应的物体上的多个物点的运动状态参数。
其中,多个采样点中的每个采样点均对应于扫描到的物体上的一个物点。
示例性地,运动状态参数可以包括测量得到的物点与点云雷达60的相对距离、相对角度、相对速度等。其中,相对角度可以为物点与点云雷达的连线和第一方向的夹角。
步骤S2:对多个采样点进行聚类,以划分出多个聚类组,其中同一聚类组内的各个采样点是扫描所述环境中同一物体得到的。
示例性地,可以采用本领域公知的任何聚类算法进行聚类,例如K均值聚类算法、均值偏移聚类算法、DBSCAN聚类算法、用GMM的最大期望聚类算法或凝聚层次聚类算法等,本发明对此不作限定。
步骤S3:根据同一聚类组内的采样点对应的物点的运动状态参数估计物体的速度(即真实相对速度)。
具体地,可以利用同一聚类组内的这些采样点的信息构造一个有噪声的超定方程,然后利用最小二乘法求解该超定方程,从而得到物体的速度。
下面阐述一下利用最小二乘方法估计v
x和v
y的求解过程。
根据物点的运动状态参数可以构造如下方程:
v
e=Av
r
其中,
v
e=[v
1 v
2 … v
I]
T
v
r=[v
x v
y]
T
根据以上方程,利用最小二乘法可以求得下列等式:
v
r=(A
TA)
-1A
Tv
e (1)
示例性地,可以对该聚类组内的各个采样点对应的物点的运动状态参数进行加权运算以得到物体的速度。
其中,加权运算的权重系数可以根据需要进行设置。示例性地,还可以包括步骤:获取各个采样点的强度信息,根据该强度信息确定加权运算的权重系数。其中,采样点的强度信息对应于雷达波在物体上相应物点处的散射强度,在物点处的散射强度越大,相应的采样点的强度越大,在物点处的散射强度越小,相应的采样点的强度越小。散射强度大的物点为强散射点,其对应的雷达回波信噪比较好,从而雷达测得的采样点信息更为准确,因此增加强散射点的权重影响,可以获得更为准确的估计结果。
其中,根据采样点的强度信息P确定加权运算的权重系数时,上述等式(1)相应地变换为:
v
r=(A
TPA)
-1A
TPv
e
示例性地,可以将每一采样点的强度信息p
i,作为该采样点对应的物点的运动状态参数的权重系数。
示例性地,还可以根据测量得到的物点相对于点云雷达60的相对速度以及物点与点云雷达的连线和第一方向的夹角求解该相对速度在该第一方向上的第一速度分量和第二方向上的第二速度分量,其 中第二方向与第一方向垂直。示例性地,第一方向可以为点云雷达所在的可移动平台与扫描到的物体的左右方向,第二方向可以为点云雷达所在的可移动平台与扫描到的物体的前后方向。
示例性地,还可以根据该第一速度分量和/或第二速度分量控制可移动平台的运动。
示例性地,可以根据第一速度分量确定可移动平台前方的物体在第一方向上的第一运动状况,以及根据第二速度分量确定可移动平台与其前方的物体在第二方向上的第二运动状况,以根据该第一运动状况和第二运动状况控制可移动平台的运动。
例如,当设置向右为第一方向的正方向时,可以根据第一速度分量为正,确定可移动平台与物体的左右距离在增加;根据第一速度分量为负,确定可移动平台与物体的左右距离在减小;根据第一速度分量为零,确定可移动平台与物体的左右距离保持不变。又例如,当设置向左为第一方向的正方向时,可以根据第一速度分量为正,确定可移动平台与物体的左右距离在减小;根据第一速度分量为负,确定可移动平台与物体的左右距离在增加;根据第一速度分量为零,确定可移动平台与物体的左右距离保持不变。
例如,当设置向后为第二方向的正方向时,可以根据第二速度分量为正,确定可移动平台与物体的前后距离在增加;根据第二速度分量为负,确定可移动平台与物体的前后距离在减小;根据第二速度分量为零,确定可移动平台与物体的前后距离保持不变。又例如,当设置向前为第二方向的正方向时,可以根据第二速度分量为正,确定可移动平台与物体的前后距离在减小;根据第二速度分量为负,确定可移动平台与物体的前后距离在增加;根据第二速度分量为零,确定可移动平台与物体的前后距离保持不变。
示例性地,还可以根据测量得到的物点与点云雷达60的相对距离以及物点与点云雷达60的连线和第一方向的夹角计算出该相对距离在第一方向上的第一相对距离和该相对距离在第二方向上的第二相对距离,以根据该第一相对距离和第二相对距离控制可移动平台的运动。
例如,当第一相对距离较小时,即可移动平台向与物体的左右距 离较小,可以控制可移动平台向远离物体的方向运动适当距离,以避免左右方向上发生碰撞;当第二相对距离较小时,即可移动平台向与物体的前后距离较小,可以控制可移动平台适当减速,加大其与物体的前后距离,从而避免前后方向上发生碰撞。
示例性地,还可以根据估计出的物体的速度以及某物点与点云雷达60的连线和第一方向的夹角,计算出该物点相对于点云雷达60的理论相对速度,并基于该理论相对速度评估点云雷达60的性能。
示例性地,还可以计算出该理论相对速度与测得的相对速度的差值,并基于该差值评估点云雷达60的性能。具体地,如果该理论相对速度与实际的相对速度的差值较小,则点云雷达60的性能较好;如果该理论相对速度与实际的相对速度的差值较大,则点云雷达60的性能较差,此时可以发出报警信号,例如声音、文字、图形等报警信号,以提示用户对点云雷达60进行调试、修理等。
示例性地,还可以计算出该差值与测得的相对速度的比值,并基于该比值评估点云雷达60的性能。具体地,如果该差值与测得的相对速度的比值较小,则点云雷达60的性能较好;如果该差值与测得的相对速度的比值较大,则点云雷达60的性能较差,此时可以发出报警信号,例如声音、文字、图形等报警信号,以提示用户对点云雷达60进行调试、修理等。
本实施例的点云雷达,测量多个采样点,并利用测量得到的多个采样点信息估计物体的速度,降低了测量噪声带来的测量偏差,提升了速度测量精度,改善了雷达对变道等速度测量要求极为准确的场景的测量性能,有力地保障了行车安全性。进一步地增加强采样点的权重影响,使得获得的估计结果更为准确。
本实施例提供了一种计算机可读介质,该计算机可读介质上存储有计算机程序,该计算机程序在运行时执行如上所述的采用点云雷达估计物体速度的方法。
根据本发明的计算机可读介质,在其上的计算机程序被执行时,能够利用由点云雷达测量得到的多个采样点信息估计物体的速度,降低了测量噪声带来的测量偏差,提升了速度测量精度,改善了雷达对 变道等速度测量要求极为准确的场景的测量性能,有力地保障了行车安全性。进一步地增加强采样点的权重影响,使得获得的估计结果更为准确。
尽管这里已经参考附图描述了示例实施例,应理解上述示例实施例仅仅是示例性的,并且不意图将本发明的范围限制于此。本领域普通技术人员可以在其中进行各种改变和修改,而不偏离本发明的范围和精神。所有这些改变和修改意在被包括在所附权利要求所要求的本发明的范围之内。
本领域普通技术人员可以意识到,结合本文中所公开的实施例描述的各示例的单元及算法步骤,能够以电子硬件、或者计算机软件和电子硬件的结合来实现。这些功能究竟以硬件还是软件方式来执行,取决于技术方案的特定应用和设计约束条件。专业技术人员可以对每个特定的应用来使用不同方法来实现所描述的功能,但是这种实现不应认为超出本发明的范围。
在本申请所提供的几个实施例中,应该理解到,所揭露的设备和方法,可以通过其它的方式实现。例如,以上所描述的设备实施例仅仅是示意性的,例如,所述单元的划分,仅仅为一种逻辑功能划分,实际实现时可以有另外的划分方式,例如多个单元或组件可以结合或者可以集成到另一个设备,或一些特征可以忽略,或不执行。
在此处所提供的说明书中,说明了大量具体细节。然而,能够理解,本发明的实施例可以在没有这些具体细节的情况下实践。在一些实例中,并未详细示出公知的方法、结构和技术,以便不模糊对本说明书的理解。
类似地,应当理解,为了精简本发明并帮助理解各个发明方面中的一个或多个,在对本发明的示例性实施例的描述中,本发明的各个特征有时被一起分组到单个实施例、图、或者对其的描述中。然而,并不应将该本发明的方法解释成反映如下意图:即所要求保护的本发明要求比在每个权利要求中所明确记载的特征更多的特征。更确切地说,如相应的权利要求书所反映的那样,其发明点在于可以用少于某个公开的单个实施例的所有特征的特征来解决相应的技术问题。因此, 遵循具体实施方式的权利要求书由此明确地并入该具体实施方式,其中每个权利要求本身都作为本发明的单独实施例。
本领域的技术人员可以理解,除了特征之间相互排斥之外,可以采用任何组合对本说明书(包括伴随的权利要求、摘要和附图)中公开的所有特征以及如此公开的任何方法或者设备的所有过程或单元进行组合。除非另外明确陈述,本说明书(包括伴随的权利要求、摘要和附图)中公开的每个特征可以由提供相同、等同或相似目的的替代特征来代替。
此外,本领域的技术人员能够理解,尽管在此所述的一些实施例包括其它实施例中所包括的某些特征而不是其它特征,但是不同实施例的特征的组合意味着处于本发明的范围之内并且形成不同的实施例。例如,在权利要求书中,所要求保护的实施例的任意之一都可以以任意的组合方式来使用。
本发明的各个部件实施例可以以硬件实现,或者以在一个或者多个处理器上运行的软件模块实现,或者以它们的组合实现。本领域的技术人员应当理解,可以在实践中使用微处理器或者数字信号处理器(DSP)来实现根据本发明实施例的物品分析设备中的一些模块的一些或者全部功能。本发明还可以实现为用于执行这里所描述的方法的一部分或者全部的装置程序(例如,计算机程序和计算机程序产品)。这样的实现本发明的程序可以存储在计算机可读介质上,或者可以具有一个或者多个信号的形式。这样的信号可以从因特网网站上下载得到,或者在载体信号上提供,或者以任何其他形式提供。
应该注意的是上述实施例对本发明进行说明而不是对本发明进行限制,并且本领域技术人员在不脱离所附权利要求的范围的情况下可设计出替换实施例。在权利要求中,不应将位于括号之间的任何参考符号构造成对权利要求的限制。单词“包含”不排除存在未列在权利要求中的元件或步骤。位于元件之前的单词“一”或“一个”不排除存在多个这样的元件。本发明可以借助于包括有若干不同元件的硬件以及借助于适当编程的计算机来实现。在列举了若干装置的单元权利要求中,这些装置中的若干个可以是通过同一个硬件项来具体体现。单词 第一、第二、以及第三等的使用不表示任何顺序。可将这些单词解释为名称。
以上所述,仅为本发明的具体实施方式或对具体实施方式的说明,本发明的保护范围并不局限于此,任何熟悉本技术领域的技术人员在本发明揭露的技术范围内,可轻易想到变化或替换,都应涵盖在本发明的保护范围之内。本发明的保护范围应以权利要求的保护范围为准。
Claims (24)
- 一种采用点云雷达估计物体速度的方法,其特征在于,所述方法包括:通过点云雷达对环境进行扫描,获取扫描得到的多个采样点对应的物体上的多个物点的运动状态参数;对多个所述采样点进行聚类,以划分出多个聚类组,其中同一聚类组内的各个所述采样点是扫描所述环境中同一物体得到的;根据所述同一聚类组内的所述采样点对应的物点的运动状态参数估计所述物体的速度。
- 如权利要求1所述的方法,其特征在于,其中所述根据所述同一聚类组内的所述采样点对应的物点的运动状态参数估计所述物体的速度,包括:对所述聚类组内的各个所述采样点对应的所述物点的运动状态参数进行加权运算以得到所述物体的速度。
- 如权利要求2所述的方法,其特征在于,所述方法还包括:获取各个所述采样点的强度信息;根据所述强度信息确定所述加权运算的权重系数。
- 如权利要求3所述的方法,其特征在于,将每一采样点的所述强度信息,作为所述采样点对应的所述物点的运动状态参数的权重系数。
- 如权利要求1-4中任一项所述的方法,其特征在于,其中所述物点的运动状态参数包括所述物点相对于所述点云雷达的相对速度以及所述物点与所述点云雷达的连线和第一方向的夹角。
- 如权利要求5所述的方法,其特征在于,所述方法还包括:根据所述相对速度和所述夹角求解所述相对速度在所述第一方向上的第一速度分量和第二方向上的第二速度分量,其中所述第二方向与所述第一方向垂直。
- 如权利要求6所述的方法,其特征在于,所述点云雷达装载于可移动平台上,所述方法还包括:根据第一速度分量和/或第二速度分量控制所述可移动平台的运动。
- 如权利要求7所述的方法,其特征在于,其中根据第一速度分量和/或第二速度分量控制所述可移动平台的运动包括:根据所述第一速度分量确定所述可移动平台前方的物体在所述第一方向上的第一运动状况,以及根据所述第二速度分量确定所述可移动平台与其前方的物体在第二方向上的第二运动状况,以根据所述第一运动状况和所述第二运动状况控制所述可移动平台的运动。
- 如权利要求7所述的方法,其特征在于,所述物点的运动状态参数还包括所述物点与所述点云雷达的相对距离,其中所述方法还包括:根据所述相对距离与所述夹角计算出所述相对距离在第一方向上的第一相对距离和所述相对距离在第二方向上的第二相对距离,以根据所述第一相对距离和第二相对距离控制所述可移动平台的运动。
- 如权利要求5所述的方法,其特征在于,所述方法还包括:根据估计出的所述物体的速度以及所述物点与所述点云雷达的连线和第一方向的所述夹角,计算出所述物点相对于所述点云雷达的理论相对速度;基于所述理论相对速度评估所述点云雷达的性能。
- 如权利要求10所述的方法,其特征在于,其中基于所述理论相对速度评估所述点云雷达的性能包括:计算出所述理论相对速度与所述相对速度的差值,并基于所述差值评估所述点云雷达的性能。
- 一种点云雷达系统,其特征在于,所述点云雷达系统包括:点云雷达,用于对环境进行扫描以得到多个采样点,与所述点云雷达耦连的处理装置,用于:获取所述多个采样点对应的物体上的多个物点的运动状态参数;对所述多个采样点进行聚类,以划分出多个聚类组,其中同一聚类组内的各个所述采样点是扫描所述环境中同一物体得到的;根据所述同一聚类组内的所述采样点对应的物点的运动状 态参数估计所述物体的速度。
- 如权利要求12所述的点云雷达系统,其特征在于,所述处理装置还用于:对所述聚类组内的各个所述采样点对应的所述物点的运动状态参数进行加权运算以得到所述物体的速度。
- 如权利要求13所述的点云雷达系统,其特征在于,所述处理装置还用于:获取各个所述采样点的强度信息;根据所述强度信息确定所述加权运算的权重系数。
- 如权利要求14所述的点云雷达系统,其特征在于,所述处理装置还用于:将每一采样点的所述强度信息,作为所述采样点对应的所述物点的运动状态参数的权重系数。
- 如权利要求12-15中任一项所述的点云雷达系统,其特征在于,其中所述物点的运动状态参数包括所述物点相对于所述点云雷达的相对速度以及所述物点与所述点云雷达的连线和第一方向的夹角。
- 如权利要求16所述的点云雷达系统,其特征在于,所述处理装置还用于:根据所述相对速度和所述夹角求解所述相对速度在所述第一方向上的第一速度分量和第二方向上的第二速度分量,其中所述第二方向与所述第一方向垂直。
- 如权利要求17所述的点云雷达系统,其特征在于,所述点云雷达装载于可移动平台上,所述处理装置还用于:根据第一速度分量和/或第二速度分量控制所述可移动平台的运动。
- 如权利要求18所述的点云雷达系统,其特征在于,所述处理装置还用于:根据所述第一速度分量确定所述可移动平台前方的物体在所述第一方向上的第一运动状况,以及根据所述第二速度分量确定所述可移动平台与其前方的物体在第二方向上的第二运动状况,以根据所述第一运动状况和所述第二运动状况控制所述可移动平台的运动。
- 如权利要求18所述的点云雷达系统,其特征在于,所述物点的运动状态参数还包括所述物点与所述点云雷达的相对距离,所述处理装置还用于:根据所述相对距离与所述夹角计算出所述相对距离在第一方向上的第一相对距离和所述相对距离在第二方向上的第二相对距离,以根据所述第一相对距离和第二相对距离控制所述可移动平台的运动。
- 如权利要求16所述的点云雷达系统,其特征在于,所述处理装置还用于:根据估计出的所述物体的速度以及所述物点与所述点云雷达的连线和第一方向的所述夹角,计算出所述物点相对于所述点云雷达的理论相对速度;基于所述理论相对速度评估所述点云雷达的性能。
- 如权利要求21所述的点云雷达系统,其特征在于,所述处理装置还用于:计算出所述理论相对速度与所述相对速度的差值,并基于所述差值评估所述点云雷达的性能。
- 一种点云雷达,其特征在于,包括:存储器,用于存储计算机程序,和与所述存储器耦连的处理器,用于执行所述计算机程序,以实现以下步骤:获取通过点云雷达对环境进行扫描得到的多个采样点对应的物体上的多个物点的运动状态参数;对多个所述采样点进行聚类,以划分出多个聚类组,其中同一聚类组内的各个所述采样点是扫描所述环境中同一物体得到的;根据所述同一聚类组内的所述采样点对应的物点的运动状态参数估计所述物体的速度。
- 一种计算机可读存储介质,其特征在于,包括计算机可执行指令,所述计算机可执行指令在由处理器执行时,能够执行如权利要求1-11中任一项所述的方法。
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Cited By (3)
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US20220319054A1 (en) * | 2021-03-01 | 2022-10-06 | Waymo Llc | Generating scene flow labels for point clouds using object labels |
CN115761215A (zh) * | 2022-11-10 | 2023-03-07 | 中国矿业大学 | 一种考虑物体表面粗糙度的隔离开关监测方法 |
CN116859360A (zh) * | 2023-09-04 | 2023-10-10 | 深圳安智杰科技有限公司 | 一种基于单帧毫米波雷达点云的运动估计方法及系统 |
Citations (5)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN109782015A (zh) * | 2019-03-21 | 2019-05-21 | 同方威视技术股份有限公司 | 激光测速方法、控制装置和激光测速仪 |
CN110082747A (zh) * | 2018-01-25 | 2019-08-02 | Aptiv技术有限公司 | 基于多个检测点来增加确定车辆位置的可靠性的方法 |
WO2020005955A1 (en) * | 2018-06-26 | 2020-01-02 | Zoox, Inc. | Radar clustering and velocity disambiguation |
US10634793B1 (en) * | 2018-12-24 | 2020-04-28 | Automotive Research & Testing Center | Lidar detection device of detecting close-distance obstacle and method thereof |
CN111260683A (zh) * | 2020-01-09 | 2020-06-09 | 合肥工业大学 | 一种三维点云数据的目标检测与跟踪方法及其装置 |
Family Cites Families (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
DE102012200139A1 (de) * | 2012-01-05 | 2013-07-11 | Robert Bosch Gmbh | Verfahren und Vorrichtung zur radunabhängigen Geschwindigkeitsmessung bei einem Fahrzeug |
WO2018137135A1 (en) * | 2017-01-24 | 2018-08-02 | SZ DJI Technology Co., Ltd. | System and method of radar-based obstacle avoidance for unmanned aerial vehicles |
EP3809159A1 (en) * | 2019-07-04 | 2021-04-21 | SZ DJI Technology Co., Ltd. | Methods of using lateral millimeter wave radar to detect lateral stationary object and measure moving speed |
CN111123253B (zh) * | 2019-12-26 | 2022-05-17 | 成都纳雷科技有限公司 | 基于自适应门限目标聚类的车辆识别方法、系统及介质 |
-
2020
- 2020-09-25 WO PCT/CN2020/117879 patent/WO2022061758A1/zh active Application Filing
- 2020-09-25 CN CN202080031333.4A patent/CN113795771A/zh active Pending
Patent Citations (5)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN110082747A (zh) * | 2018-01-25 | 2019-08-02 | Aptiv技术有限公司 | 基于多个检测点来增加确定车辆位置的可靠性的方法 |
WO2020005955A1 (en) * | 2018-06-26 | 2020-01-02 | Zoox, Inc. | Radar clustering and velocity disambiguation |
US10634793B1 (en) * | 2018-12-24 | 2020-04-28 | Automotive Research & Testing Center | Lidar detection device of detecting close-distance obstacle and method thereof |
CN109782015A (zh) * | 2019-03-21 | 2019-05-21 | 同方威视技术股份有限公司 | 激光测速方法、控制装置和激光测速仪 |
CN111260683A (zh) * | 2020-01-09 | 2020-06-09 | 合肥工业大学 | 一种三维点云数据的目标检测与跟踪方法及其装置 |
Cited By (5)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20220319054A1 (en) * | 2021-03-01 | 2022-10-06 | Waymo Llc | Generating scene flow labels for point clouds using object labels |
CN115761215A (zh) * | 2022-11-10 | 2023-03-07 | 中国矿业大学 | 一种考虑物体表面粗糙度的隔离开关监测方法 |
CN115761215B (zh) * | 2022-11-10 | 2024-04-26 | 中国矿业大学 | 一种考虑物体表面粗糙度的隔离开关监测方法 |
CN116859360A (zh) * | 2023-09-04 | 2023-10-10 | 深圳安智杰科技有限公司 | 一种基于单帧毫米波雷达点云的运动估计方法及系统 |
CN116859360B (zh) * | 2023-09-04 | 2023-11-24 | 深圳安智杰科技有限公司 | 一种基于单帧毫米波雷达点云的运动估计方法及系统 |
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