WO2022217988A1 - Procédé et appareil de détermination de schéma de configuration de capteur, dispositif informatique, support de stockage et programme - Google Patents

Procédé et appareil de détermination de schéma de configuration de capteur, dispositif informatique, support de stockage et programme Download PDF

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WO2022217988A1
WO2022217988A1 PCT/CN2022/071455 CN2022071455W WO2022217988A1 WO 2022217988 A1 WO2022217988 A1 WO 2022217988A1 CN 2022071455 W CN2022071455 W CN 2022071455W WO 2022217988 A1 WO2022217988 A1 WO 2022217988A1
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sensor configuration
configuration scheme
sensor
screened
measurement data
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PCT/CN2022/071455
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Chinese (zh)
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马涛
刘知正
李怡康
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上海商汤智能科技有限公司
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T1/00—General purpose image data processing
    • G06T1/0007—Image acquisition
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T19/00—Manipulating 3D models or images for computer graphics
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/70—Determining position or orientation of objects or cameras
    • 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
    • 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 disclosure relates to the technical field of intelligent driving, and in particular, to a method, device, computer equipment, storage medium and program for determining a sensor configuration scheme.
  • various sensors such as radar, cameras, etc.
  • the detection results of the sensors are used to identify obstacles.
  • the selection and installation position of the sensor directly affect the accuracy of obstacle recognition.
  • human experience and rules are generally mainly relied on.
  • this method requires repeated adjustments and is inefficient when deploying sensors.
  • Embodiments of the present disclosure provide at least a method, apparatus, computer device, storage medium, and program for determining a sensor configuration scheme.
  • Embodiments of the present disclosure provide a method for determining a sensor configuration scheme, including:
  • the simulation measurement data corresponding to each sensor configuration scheme to be screened is the data of the target object measured by the sensor in the sensor configuration scheme;
  • conditional entropy of the sensor configuration scheme is determined; the conditional entropy of a filtered sensor configuration scheme is used to characterize the measurement results of the sensors in the sensor configuration scheme in the sensor Stability under simulated measurement data corresponding to the configuration scheme;
  • a target sensor configuration scheme is determined from the plurality of sensor configuration schemes to be screened. In this way, each sensor configuration scheme can be judged by quantitative indicators, and then the optimal sensor configuration scheme can be selected through the conditional entropy of each sensor configuration scheme.
  • Embodiments of the present disclosure also provide a device for determining a sensor configuration scheme, including:
  • an acquisition module configured to acquire multiple sensor configuration schemes to be screened
  • the first determination module is configured to determine the simulated measurement data corresponding to each sensor configuration scheme to be screened; the simulated measurement data corresponding to one sensor configuration scheme to be screened is the data of the target object measured by the sensor in the sensor configuration scheme ;
  • the second determination module is configured to determine the conditional entropy of each sensor configuration scheme to be screened based on the simulation measurement data corresponding to the sensor configuration scheme; the conditional entropy of a sensor configuration scheme to be screened is used to characterize the sensor configuration scheme. The stability of the measurement results of the sensor under the simulated measurement data corresponding to the sensor configuration scheme;
  • the selection module is configured to determine a target sensor configuration scheme from the plurality of sensor configuration schemes to be screened based on the determined conditional entropy of each sensor configuration scheme to be screened.
  • An embodiment of the present disclosure further provides a computer device, including: a processor, a memory, and a bus, where the memory stores machine-readable instructions executable by the processor, and when the computer device runs, the processor and the The memories communicate with each other through a bus, and when the machine-readable instructions are executed by the processor, the steps of the method for determining a sensor configuration scheme described in any of the foregoing embodiments are executed.
  • Embodiments of the present disclosure further provide a computer-readable storage medium, where a computer program is stored on the computer-readable storage medium, and when the computer program is run by a processor, the steps of the method for determining a sensor configuration scheme described in any of the foregoing embodiments are executed. .
  • Embodiments of the present disclosure also provide a computer program, where the computer program includes computer-readable codes, and when the computer-readable codes are executed in an electronic device, the processor of the electronic device executes any of the above.
  • FIG. 1 shows a schematic flowchart of a method for determining a sensor configuration scheme provided by an embodiment of the present disclosure
  • FIG. 2 shows a schematic diagram of a system architecture of a method for determining a sensor configuration scheme provided by an embodiment of the present disclosure
  • FIG. 3 shows a schematic flowchart of a method for obtaining a plurality of sensor configuration schemes to be screened in the method for determining a sensor configuration scheme provided by an embodiment of the present disclosure
  • FIG. 4 shows a schematic diagram of target object voxelization provided by an embodiment of the present disclosure
  • FIG. 5 shows a schematic structural diagram of an apparatus for determining a sensor configuration scheme provided by an embodiment of the present disclosure
  • FIG. 6 shows a schematic structural diagram of a computer device provided by an embodiment of the present disclosure.
  • the rules may be to minimize blind spots and improve the perception range.
  • experience and rules cannot be converted into specific data, so each sensor configuration scheme cannot be judged intuitively, which leads to low efficiency in sensor deployment.
  • the execution subject of the method for determining a sensor configuration scheme provided by the embodiment of the present disclosure is generally a computer with a certain computing capability.
  • equipment the computer equipment for example includes: terminal equipment or server or other processing equipment, the terminal equipment can be user equipment (User Equipment, UE), mobile equipment, user terminal, terminal, cellular phone, cordless phone, personal digital assistant (Personal Digital Assistant) Assistant, PDA), handheld devices, computing devices, in-vehicle devices, wearable devices, etc.
  • the sensor configuration scheme determination method may be implemented by the processor invoking computer-readable instructions stored in the memory.
  • Fig. 1 is a flowchart of a method for determining a sensor configuration scheme provided by an embodiment of the present disclosure, the method includes steps 101 to 104, wherein:
  • Step 101 Acquire a plurality of sensor configuration solutions to be screened.
  • Step 102 Determine the simulated measurement data corresponding to each sensor configuration scheme to be screened; the simulated measurement data corresponding to one sensor configuration scheme to be screened is the data of the target object measured by the sensors in the sensor configuration scheme.
  • Step 103 Determine the conditional entropy of the sensor configuration scheme based on the simulation measurement data corresponding to each sensor configuration scheme to be screened; the conditional entropy of a sensor configuration scheme to be screened is used to characterize the measurement results of the sensors in the sensor configuration scheme Stability under simulated measurement data corresponding to this sensor configuration.
  • Step 104 based on the determined conditional entropy of each sensor configuration scheme to be screened, from the plurality of sensor configuration schemes to be screened, determine a target sensor configuration scheme.
  • a plurality of sensor configuration schemes to be screened can be obtained, different sensor configuration schemes correspond to different simulated measurement data, and then based on the simulated measurement data corresponding to different sensor configuration schemes, Determine the conditional entropy of different sensor configuration schemes respectively.
  • the conditional entropy can be understood as the stability of another variable under the condition of a known random variable. Referring to this scheme, the conditional entropy of the sensor configuration scheme is the sensor configuration scheme.
  • the conditional entropy of the sensor configuration scheme can also be understood as the sensor measurement results in different sensor configuration schemes. In this way, each sensor configuration scheme can be judged by quantitative indicators, and then the optimal sensor configuration scheme can be selected through the conditional entropy of each sensor configuration scheme.
  • FIG. 2 shows a schematic diagram of a system architecture to which a method for determining a sensor configuration scheme according to an embodiment of the present disclosure can be applied; as shown in FIG. 2 , the system architecture includes: a plurality of sensor configuration scheme acquisition terminals 201 to be screened, a network 202 and a control terminal 203.
  • the system architecture includes: a plurality of sensor configuration scheme acquisition terminals 201 to be screened, a network 202 and a control terminal 203.
  • a plurality of sensor configuration scheme acquisition terminals 201 to be screened and the control terminal 203 establish communication connections through the network 202, and the plurality of sensor configuration scheme acquisition terminals 201 to be screened report to the control terminal 203 through the network 202.
  • the control terminal 203 determines the simulated measurement data corresponding to each sensor configuration scheme to be screened in response to the plurality of sensor configuration schemes to be screened, and then, based on the simulated measurement data, determines each simulated measurement data The conditional entropy of the corresponding sensor configuration scheme; thirdly, based on the determined conditional entropy of each sensor configuration scheme to be screened, from the plurality of sensor configuration schemes to be screened, the target sensor configuration scheme is determined. Finally, the control terminal 203 uploads the target sensor configuration scheme to the network 202 , and sends the target sensor configuration scheme acquisition terminal 201 through the network 202 to a plurality of sensor configuration scheme acquisition terminals 201 to be screened.
  • the acquisition terminals 201 of the multiple sensor configuration solutions to be screened may include an image acquisition device, and the control terminal 203 may include a vision processing device or a remote server with visual information processing capability.
  • Network 202 may employ wired or wireless connections.
  • the control terminal 203 is a vision processing device
  • the acquisition terminals 201 of a plurality of sensor configuration solutions to be screened can be connected to the vision processing device through wired connection, such as data communication through a bus; when the control terminal 203 is a remote server
  • the acquisition terminals 201 of a plurality of sensor configuration solutions to be screened can perform data interaction with a remote server through a wireless network.
  • the acquisition terminals 201 of the multiple sensor configuration solutions to be screened may be a vision processing device with a video capture module, or a host with a camera.
  • the display method in the augmented reality scenario of the embodiment of the present disclosure may be executed by a plurality of sensor configuration solution acquisition terminals 201 to be screened, and the above-mentioned system architecture may not include the network 202 and the control terminal 203 .
  • the sensor configuration solution may be a sensor deployment solution in an automatic driving device, including sensor installation information and sensor internal parameter information.
  • the sensor installation information includes the installation position of the sensor in the predefined sensing space (for example, the three-dimensional coordinates in the sensing space) and the installation orientation (for example, the rotation matrix); wherein, the sensing space It is the range of the area that needs to be sensed around the automatic driving device.
  • conditional entropy is used to reflect the stability of the measurement results of the sensor under the simulated measurement data, therefore, when determining the conditional entropy, only The conditional entropy in the perceptual space needs to be determined.
  • the center of the automatic driving device can be used as the intersection of the body diagonals, and the perception space corresponding to the automatic driving device can be set according to the preset length, width and height. Due to the different perception requirements of autonomous driving devices of different sizes, for example, for a small car, there is no need to perceive objects (such as signs, etc.) in a higher space, while for a car with a higher height, Objects in higher spaces need to be sensed; for automatic driving devices with automatic parking function, it is necessary to pay attention to the objects in the space at the rear of the vehicle as much as possible; for automatic driving devices without automatic parking function, Therefore, in order to meet the perception requirements of different automatic driving devices and reduce the amount of calculation in the selection process of sensor configuration solutions, it can be used for different sizes of automatic driving devices. Set different sizes of perceived space. Exemplarily, the length, width and height of the perception space may be proportional to the length, width and height of the automatic driving device.
  • a perception space of the same size is set for all automatic driving devices, but different weights are set for different positions in the perception space, and the weights are used to represent the importance of detection of objects appearing at the position for the automatic driving device.
  • the weight of the rear of the vehicle may be set lower.
  • the sensor internal parameter information may include vertical angular resolution and horizontal angular resolution; when the sensor includes an image acquisition device, the sensor internal parameter information may include an internal parameter matrix of the image acquisition device,
  • the image acquisition device may be, for example, a camera.
  • Different types of sensors correspond to different simulated measurement data, so that through different types of simulated measurement data, a sensor configuration scheme for various types of sensors can be determined.
  • the method shown in FIG. 3 may be referred to, including the following steps:
  • Step 301 Obtain initial installation positions of multiple sensors.
  • Step 302 Offset the initial installation position of each sensor according to the set offset to obtain a plurality of installation positions to be screened.
  • Step 303 Combine multiple installation positions of different sensors to be screened to obtain a plurality of sensor configuration solutions to be screened.
  • the initial installation position may refer to the approximate installation position.
  • a lidar needs to be installed on the roof of an autonomous vehicle, but the precise and optimal installation position cannot be determined. Therefore, any position on the roof can be installed.
  • a location is set as the initial installation location, and then a location search is performed in step 302 to obtain a plurality of installation locations to be screened, and then the optimal installation location is determined.
  • the offset may refer to an offset step, and different types of sensors may have different offsets when performing offset.
  • each time the initial installation position is offset it can be offset from the initial installation position, and the direction of the offset can be different.
  • Each time the initial installation position is offset one can be obtained The installation location to be filtered.
  • the Nth offset when there is only one offset, when the initial installation position is offset according to the offset, the Nth offset may be performed on the basis of the Nth offset installation position. +1 offset, the first offset is based on the initial installation position, the second offset is based on the first offset installation position... and so on until Complete the offset for a preset number of times, and N is a positive integer greater than or equal to 1.
  • the multiple offsets may be sorted in descending order, and then the initial installation position may be offset based on the largest offset (for example, you can offset preset times) to obtain multiple intermediate offset installation positions. Then use the multiple intermediate offset installation positions as the initial installation positions, and then use the second offset in the sorting result to offset... and so on, until the offset is based on each offset.
  • the multiple sensor configuration schemes obtained after this offset can be determined, and then the sensor configuration scheme with the smallest conditional entropy among the multiple sensor configuration schemes is installed.
  • the position is used as the initial installation position, and the above process is performed again until the sensor configuration scheme corresponding to the smallest offset is obtained.
  • the acquisition of multiple sensor configuration schemes to be screened in step 101 may be to acquire the sensor configuration scheme corresponding to the smallest offset.
  • multiple installation positions of different sensors to be screened may also be combined, and then combined with at least one sensor internal parameter information preconfigured to obtain multiple sensor configuration solutions to be screened.
  • the preconfigured at least one sensor internal parameter information may refer to the internal parameter information of different types of sensors, for example, for lidar, there may be a 64-line radar, a 32-line radar, and the sensor internal parameter information corresponding to different types of sensors. different.
  • sensors include sensors of different categories, such as radars, cameras, etc., and sensors of the same type with different internal reference information, such as cameras with different internal reference information, or Radar with different internal reference information.
  • the automatic search for the sensor configuration scheme can be realized, and then the selection of the optimal configuration scheme can be realized through the conditional entropy corresponding to the searched different sensor configuration schemes.
  • step 102 For step 102,
  • the simulated measurement data is data of the target object measured by the sensor in the sensor configuration scheme.
  • the simulated measurement data includes the number of point cloud points reflected by the target object;
  • the simulated measurement data includes The area occupied by the target object in the image captured by the image acquisition device.
  • the target object may refer to a preset object that needs to be perceived in the perception space, and may include, for example, vehicles, pedestrians, and the like.
  • the target objects in the pre-defined perception space may be Voxelization processing to obtain multiple voxels corresponding to the target object; then for each sensor configuration scheme to be screened, based on the sensor configuration scheme and the position coordinates of the multiple voxels corresponding to the target object in the sensing space, Determine the simulation measurement data corresponding to the sensor configuration scheme.
  • the process of voxelizing the target object can be understood as dividing the surface of the target object into cubes of preset size.
  • An exemplary process of voxelization is shown in FIG. 4 .
  • the simulation measurement data can be determined directly according to the position coordinates of the voxel of the target object, which can speed up the calculation speed of the simulation measurement data.
  • the sensor configuration scheme and the position coordinates of multiple voxels corresponding to the target object in the sensing space when determining the simulated measurement data corresponding to the sensor configuration scheme, may include the following steps:
  • Step a Based on the installation position, vertical angular resolution and horizontal angular resolution of the lidar, determine a rotation matrix corresponding to any laser beam of the lidar, where the rotation matrix is used to represent the rotation matrix of the laser beam. launch direction.
  • the emission direction of the laser beam can be represented by a rotation matrix, and an exemplary calculation can be performed by the following formula:
  • V G R -1 ⁇ [sin( ⁇ )cos( ⁇ ),sin( ⁇ )sin ⁇ ,cos( ⁇ )] T (1);
  • V G represents the rotation matrix of the laser beam
  • R represents the rotation matrix of the lidar
  • ⁇ represents the vertical detection angle
  • ⁇ represents the horizontal detection angle
  • the horizontal detection angle is calculated according to the horizontal angular resolution
  • the vertical detection angle is based on The vertical angular resolution is calculated.
  • the horizontal detection angle range of the lidar is -90° to 90°
  • the horizontal angle resolution is 10°
  • the vertical detection angle range is 0° to -60°
  • the horizontal angle resolution is 10°
  • Step b Based on the rotation matrix corresponding to any one of the laser beams, and the position coordinates of the plurality of voxels corresponding to the target object in the sensing space, determine the number of point cloud points reflected by the target object. number.
  • each laser beam can be regarded as a ray with the position of the lidar as the origin. is the direction of the laser beam.
  • each laser beam can be regarded as a directed line segment with the position of the lidar as the origin and the detection distance as the length, and the direction of the line segment is the direction of each laser. The direction of the beam.
  • the distance between the position coordinates corresponding to each voxel and any of the laser beams can be calculated (for example, the distance from the point to the line, or the distance from the point to the ray), if the distance is less than the preset distance, then determine the The laser beam falls on the voxel, which has a reflected point cloud.
  • the preset target voxels to be counted under the relative position relationship can be determined according to the relative position relationship between the target object and the lidar, and then the target voxel and the relative position relationship can be calculated.
  • the distance between the individual laser beams Exemplarily, if the target object is a target vehicle, and the relative positional relationship between the target vehicle and the lidar is longitudinally parallel, the lidar can only detect the rear of the vehicle during the detection process, so when determining the simulated measurement data, only Determine the distance between the voxel at the rear of the vehicle and the laser beam.
  • the sensor configuration scheme includes the installation information of the image acquisition device and the internal parameter matrix of the image acquisition device, based on the sensor configuration scheme and the position coordinates of the plurality of voxels corresponding to the target object in the perception space , the following steps are included when determining the simulated measurement data corresponding to the sensor configuration scheme:
  • Step a Based on the installation information of the image acquisition device and the internal parameter matrix, the position coordinates of the plurality of voxels corresponding to the target object in the perception space are converted into the image coordinate system corresponding to the image acquisition device. , to obtain the target pixels corresponding to the plurality of voxels.
  • p C represents the two-dimensional coordinate corresponding to the voxel in the image coordinate system
  • p G represents the three-dimensional coordinate of the voxel in the perceptual space
  • K represents the internal parameter matrix of the image acquisition device
  • R represents the installation position of the image acquisition device ( That is, the three-dimensional coordinates in the perception space)
  • t represents the rotation matrix of the image acquisition device.
  • step b the area of the location area formed by the target pixels is taken as the area occupied by the target object in the image captured by the image acquisition device.
  • the pre-set key voxels under the relative position relationship can be determined according to the relative position relationship between the target object and the image acquisition device, and then the key voxels can be determined based on the relative position relationship between the target object and the image acquisition device.
  • the above formula (2) is converted into the image coordinate system, and the converted target pixels are connected to each other, and the area of the connected area is taken as the area occupied by the target object in the image captured by the image acquisition device. .
  • the image acquisition device can only photograph the rear of the vehicle during the detection process. Therefore, when determining the simulated measurement data, It can be determined that the voxels corresponding to the four vertices of the rear of the car are the target voxels, and the target voxels are converted into the image coordinate system to obtain four target pixels, and the four target pixels are connected to form the area.
  • the area is the area occupied by the target vehicle in the image captured by the image acquisition device.
  • conditional entropy can be understood as the stability or certainty of a variable under the condition of another known random variable.
  • the conditional entropy described in the embodiments of the present disclosure can be the conditional entropy in a special scenario, or It can be called perceptual entropy.
  • conditional entropy can be as follows:
  • V) represents the stability of the value of the variable U under the variable V.
  • formula (3) can be expressed in the following form:
  • V) - ⁇ v ⁇ u p(u
  • v))dup(v)dv E v ⁇ pV H(U
  • V) can be expressed as the expected value of the variable U when the variable V takes the value v when the variable v follows the pV distribution.
  • the detection result of the sensor is the target object detected by the sensor, and the selection of the sensor and the installation position of the sensor affect the detection result of the sensor, that is, the configuration scheme of the sensor affects the detection result of the sensor.
  • the simulated measurement data is also uniquely determined, that is, the simulated measurement data can indirectly represent the sensor configuration scheme, so it is the simulated measurement data that affects the sensor detection results.
  • conditional entropy formula in the embodiment of the present disclosure can be expressed as follows:
  • q represents the parameters in the sensor configuration scheme, including sensor internal parameter information and sensor installation information
  • m represents the simulated measurement data corresponding to the voxel
  • M represents the distribution of the simulated measurement data corresponding to the target object
  • S represents the target measured by the sensor.
  • the change of the target object is mainly in the x direction and the y direction during the driving process of the autonomous vehicle, and the value of the z direction is a fixed value, it can only include (s x , s y ).
  • conditional entropy can be expressed as the expectation when s follows the ps distribution.
  • the prior distribution of the voxel s needs to be determined.
  • the prior distribution can be determined by counting a large number of data sets, that is, the distribution of the positions of the perceived target objects in the perception space.
  • the voxel s detected by the sensor obeys the Gaussian distribution
  • the Gaussian distribution can be expressed by the following formula:
  • formula (6) can be expressed by the following formula:
  • ⁇ represents the standard deviation of the Gaussian distribution.
  • a and b are preset linear transformation coefficients. Different types of sensors have different linear transformation coefficients.
  • the linear transformation coefficients corresponding to lidar can be a 1 and b 1
  • the linear transformation coefficients corresponding to the image acquisition device can be a 2 , b 2 .
  • the sensor configuration scheme includes installation information of the multiple sensors and sensor internal parameter information.
  • the conditional entropy of each sensor configuration scheme is determined based on the simulated measurement data corresponding to the sensor configuration scheme, different fusion methods may be performed based on the different types of sensors in the sensor configuration scheme.
  • a sensor configuration solution to be screened is a configuration solution for multiple lidars, that is, the sensor configuration solution to be screened only includes lidars (that is, the sensor configuration solution does not include an image acquisition device), Then, based on the simulated measurement data of each lidar in the sensor configuration scheme, the target simulation measurement data corresponding to the sensor configuration scheme can be determined; then based on the target simulation measurement data, the conditional entropy of the sensor configuration scheme can be determined.
  • m i represents the simulated measurement data of the ith sensor, i traverses all sensors, and m fused represents the simulated measurement data of the target.
  • this fusion method is to directly sum the simulated measurement data corresponding to each sensor, if the sensor configuration scheme is a configuration scheme for multiple image acquisition devices, due to the different deployment positions of different image acquisition devices, the captured images will also be different. It is definitely different, so the images captured by different image acquisition devices cannot be directly added.
  • the sensor configuration scheme is a configuration scheme for at least one image acquisition device and at least one lidar, since the data types of the simulated measurement data of the image acquisition device and the simulated measurement data of the lidar are not the same, they cannot be directly added.
  • the simulated measurement data of different lidars is point cloud data
  • the point cloud data is used to reflect the imaging of the object.
  • the point cloud becomes denser, so the detection result of the target object can be more accurate.
  • the target simulation measurement data m fused can be brought into the formula (11) to determine the conditional entropy of the sensor configuration scheme.
  • the sensor configuration solution is a configuration solution for multiple image capture devices, or a configuration solution for at least one image capture device and at least one lidar, or a configuration solution for multiple lasers
  • a radar configuration scheme first, based on the simulated measurement data of any sensor corresponding to the sensor configuration scheme, determine the standard deviation of the Gaussian distribution to which the detected object corresponding to the sensor corresponds; Under the sensor configuration scheme, the standard deviations corresponding to a plurality of sensors are fused to obtain a target standard deviation; and then the conditional entropy of the sensor configuration scheme is determined based on the target standard deviation.
  • the Gaussian distribution obeyed by the detected object corresponding to the sensor can be understood as the distribution of the voxels s detected by the sensor after the sensor is installed on the autonomous vehicle according to the sensor configuration scheme.
  • the Simulate the measurement data determine the AP corresponding to any sensor (bring into formula (10)), and then, based on the AP corresponding to the any sensor, determine the Gaussian distribution that the detected object corresponding to the any sensor obeys The standard deviation of (bring into equation (9)).
  • the fusion can be performed by the following formula:
  • ⁇ i represents the standard deviation of the ith sensor, i is taken over all sensors, and ⁇ fused represents the target standard deviation.
  • the target standard deviation ⁇ fused can be brought into formula (8) to obtain the conditional entropy of the sensor configuration scheme.
  • Different types of sensors can use different data fusion methods, and then determine the conditional entropy of the sensor configuration scheme based on the fused data (here refers to the target simulation measurement data or target standard deviation), so it can be realized for multiple types of sensors.
  • the installation information and internal reference information are determined.
  • step 104 For step 104,
  • the conditional entropy of a sensor configuration scheme is used to characterize the stability of the measurement results of the sensors in the sensor configuration scheme under the simulated measurement data corresponding to the sensor configuration scheme.
  • the smallest sensor configuration scheme to be screened is used as the target sensor configuration scheme.
  • the writing order of each step does not mean a strict execution order but constitutes any limitation on the implementation process, and the execution order of each step should be based on its function and possible intrinsic Logical OK.
  • the embodiments of the present disclosure also provide a device for determining a sensor configuration scheme corresponding to the method for determining a sensor configuration scheme. Since the principle of the device in the embodiment of the present disclosure for solving problems is the same as the above-mentioned method for determining a sensor configuration scheme in the embodiment of the present disclosure Similarly, the implementation of the apparatus can be referred to the implementation of the method.
  • the apparatus includes: an acquisition module 501 , a first determination module 502 , a second determination module 503 , and a selection module 504 ; wherein ,
  • the obtaining module 501 is configured to obtain a plurality of sensor configuration schemes to be screened;
  • the first determination module 502 is configured to determine the simulated measurement data corresponding to each sensor configuration scheme to be screened; the simulated measurement data corresponding to one sensor configuration scheme to be screened is the target object measured by the sensor in the sensor configuration scheme. data;
  • the second determination module 503 is configured to determine the conditional entropy of the sensor configuration scheme based on the simulation measurement data corresponding to each sensor configuration scheme to be screened; the conditional entropy configuration of a sensor configuration scheme to be screened is configured to characterize the sensor configuration The stability of the measurement results of the sensors in the scheme under the simulated measurement data corresponding to the sensor configuration scheme;
  • the selection module 504 is configured to determine a target sensor configuration scheme from the plurality of sensor configuration schemes to be screened based on the determined conditional entropy of each sensor configuration scheme to be screened.
  • the sensor configuration solution is a sensor deployment solution in an automatic driving device
  • the sensor configuration scheme includes sensor installation information and sensor internal parameter information
  • the sensor installation information includes installation positions and installation orientations of a plurality of sensors in a predefined sensing space; wherein the sensing space is a range of an area around the automatic driving device that needs to be sensed.
  • the acquiring module 501 when acquiring a plurality of sensor configuration solutions to be screened, is configured to:
  • a plurality of installation positions to be screened of different sensors are combined to obtain the configuration solutions of the plurality of sensors to be screened.
  • the simulated measurement data when the sensor includes an image acquisition device, includes an area occupied by the target object in an image captured by the image acquisition device.
  • the simulated measurement data when the sensor includes a lidar, includes the number of point cloud points reflected by the target object.
  • the first determining module 502 when determining the simulated measurement data corresponding to each sensor configuration scheme to be screened, is configured to:
  • Voxelization is performed on the target object in the predefined perception space to obtain multiple voxels corresponding to the target object;
  • the simulated measurement data corresponding to the sensor configuration scheme is determined.
  • the first determining module 502 when the sensor configuration scheme includes the installation position of the lidar, and the vertical angular resolution and the horizontal angular resolution of the lidar, the first determining module 502, based on the The sensor configuration scheme and the position coordinates of the multiple voxels corresponding to the target object in the sensing space, when determining the simulated measurement data corresponding to the sensor configuration scheme, the configuration is as follows:
  • the number of point cloud points reflected by the target object is determined based on the rotation matrix corresponding to any one of the laser beams and the position coordinates of the plurality of voxels corresponding to the target object in the sensing space.
  • the first determination module 502 based on the sensor configuration scheme and the The position coordinates of the multiple voxels corresponding to the target object in the sensing space, when determining the simulated measurement data corresponding to the sensor configuration scheme, are configured as:
  • the position coordinates of the plurality of voxels corresponding to the target object in the perception space are converted into the image coordinate system corresponding to the image acquisition device to obtain the target pixels corresponding to the plurality of voxels;
  • the area of the location area formed by the target pixel points is taken as the area occupied by the target object in the image captured by the image acquisition device.
  • the second determining module 503 is configured to use the following method to determine the conditional entropy of each sensor configuration scheme:
  • lidar In the case that only lidar is included in a sensor configuration scheme to be screened, based on the simulated measurement data of each lidar in the sensor configuration scheme, determine the target simulation measurement data corresponding to the sensor configuration scheme;
  • the conditional entropy of the sensor configuration scheme is determined.
  • the second determining module 503 is configured to use the following method to determine the conditional entropy of each sensor configuration scheme:
  • any sensor configuration scheme to be screened based on the simulated measurement data of any sensor in the sensor configuration scheme, determine the standard deviation of the Gaussian distribution that the object detected by the sensor obeys;
  • the conditional entropy of the sensor configuration is determined.
  • a schematic structural diagram of a computer device 600 provided by an embodiment of the present disclosure includes a processor 601 , a memory 602 , and a bus 603 .
  • the memory 602 is configured to store execution instructions, including the memory 6021 and the external memory 6022; the memory 6021 here is also called internal memory, and is configured to temporarily store the operation data in the processor 601 and the data exchanged with the external memory 6022 such as the hard disk,
  • the processor 601 exchanges data with the external memory 6022 through the memory 6021.
  • the processor 601 communicates with the memory 602 through the bus 603, so that the processor 601 executes the following instructions:
  • the simulation measurement data corresponding to each sensor configuration scheme to be screened is the data of the target object measured by the sensor in the sensor configuration scheme;
  • the conditional entropy of the sensor configuration scheme is determined; the conditional entropy of a sensor configuration scheme to be screened is used to characterize that the measurement results of the sensors in the sensor configuration scheme are in the sensor Stability under simulated measurement data corresponding to the configuration scheme;
  • a target sensor configuration scheme is determined from the plurality of sensor configuration schemes to be screened.
  • Embodiments of the present disclosure further provide a computer-readable storage medium, where a computer program is stored on the computer-readable storage medium.
  • a computer program is stored on the computer-readable storage medium.
  • the storage medium may be a volatile or non-volatile computer-readable storage medium.
  • the embodiments of the present disclosure further provide a computer program product, the computer product carries program codes, and the instructions included in the program codes can be configured to execute the steps of the sensor configuration scheme determination method described in the above method embodiments.
  • the above-mentioned computer program product can be realized by means of hardware, software or a combination thereof.
  • the computer program product may be embodied as a computer storage medium, and in another optional embodiment, the computer program product may be embodied as a software product, such as a software development kit (SoftwAPe Development Kit, SDK), etc. Wait.
  • a software development kit SoftwAPe Development Kit, SDK
  • the units described as separate components may or may not be physically separated, and components displayed as units may or may not be physical units, that is, may be located in one place, or may be distributed to multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution in this embodiment.
  • each functional unit in each embodiment of the present disclosure may be integrated into one processing unit, or each unit may exist physically alone, or two or more units may be integrated into one unit.
  • the technical solutions of the embodiments of the present disclosure are essentially or contribute to the prior art or parts of the technical solutions may be embodied in the form of software products, and the computer software products are stored in a storage medium , including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present disclosure.
  • the aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory, ROM), random access memory (Random Access Memory, RAM), magnetic disk or optical disk and other media that can store program codes .
  • the embodiments of the present application provide a method, device, computer equipment and storage medium for determining a sensor configuration scheme, including: acquiring a plurality of sensor configuration schemes to be screened; determining simulation measurement data corresponding to each sensor configuration scheme to be screened; a The simulated measurement data corresponding to the sensor configuration scheme to be screened is the data of the target object measured by the sensors in the sensor configuration scheme; based on the simulated measurement data corresponding to each sensor configuration scheme to be screened, the conditions of the sensor configuration scheme are determined Entropy; the conditional entropy of a sensor configuration scheme to be screened is used to characterize the stability of the measurement results of the sensors in the sensor configuration scheme under the simulated measurement data corresponding to the sensor configuration scheme; based on the determined sensor configuration schemes to be screened The conditional entropy of the target sensor configuration scheme is determined from the plurality of sensor configuration schemes to be screened.

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Abstract

Conformément à des modes de réalisation, la présente invention concerne un procédé et un appareil de détermination de schéma de configuration de capteur, un dispositif informatique, un support de stockage, ainsi qu'un programme. Le procédé consiste à : obtenir une pluralité de schémas de configuration de capteur à cribler ; déterminer des données de mesure de simulation correspondant à chacun desdits schémas de configuration de capteur, les données de mesure de simulation correspondant à un schéma de configuration de capteur à cribler étant les données d'un objet cible mesurées par un capteur dans le schéma de configuration de capteur ; déterminer l'entropie conditionnelle du schéma de configuration de capteur sur la base des données de mesure de simulation correspondant à chacun desdits schémas de configuration de capteur, l'entropie conditionnelle d'un schéma de configuration de capteur à cribler étant utilisée pour représenter la stabilité du résultat de mesure du capteur dans le schéma de configuration de capteur sous les données de mesure de simulation correspondant au schéma de configuration de capteur ; et déterminer, sur la base de l'entropie conditionnelle déterminée de chacun desdits schémas de configuration de capteur, un schéma de configuration de capteur cible parmi la pluralité de schémas de configuration de capteur à cribler.
PCT/CN2022/071455 2021-04-13 2022-01-11 Procédé et appareil de détermination de schéma de configuration de capteur, dispositif informatique, support de stockage et programme WO2022217988A1 (fr)

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CN114485398B (zh) * 2022-01-17 2023-03-28 武汉精立电子技术有限公司 光学检测方案生成方法、存储介质、电子设备及系统
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