Abstract: The method comprises: creating a 3D scenario model of a calibration scenario, based on data of the calibration scenario collected by an already-calibrated 3D scanner; obtaining point cloud data collected by a to-be-calibrated multi-line laser radar at a plurality of position points in the calibration scenario respectively; aligning the point cloud data collected by the multi-line laser radar at the respective point points and point cloud data in the 3D scenario model into the same coordinate system on a principle that the respective position points are aligned with corresponding position points in the 3D scenario model; building a target function between data of points collected by the multi-line laser radar and data of matching points nearest to the points in the 3D scenario model, under the same coordinate system; calibrating the parameters of the multi-line laser radar according to the target function.

Abstract: The present application discloses a method, an apparatus, a system, and a storage medium for calibrating an exterior parameter of an on-board camera, relating to the field of autonomous driving technologies. A specific implementation scheme of the method in the application is: preprocessing two frames of images of a former frame and a latter frame collected by the on-board camera; performing feature point matching on the two preprocessed frames of images to obtain matched feature points; determining a moving posture of the on-board camera according to the matched feature points; determining a conversion relationship between a vehicle coordinate system and an on-board camera coordinate system according to the moving posture, and obtaining the external parameter of the on-board camera relative to a vehicle body.

Abstract: Embodiments of the present disclosure provide a method and a device for detecting a precision of an internal parameter of a laser radar, a related apparatus and a medium. The method may include the following steps. Point cloud data collected by the laser radar arranged on an autonomous mobile carrier travelling on a flat road is obtained. A three-dimensional scene reconstruction is performed based on the point cloud data collected to obtain a point cloud model of a three-dimensional scene. The point cloud model of the three-dimensional scene is divided to obtain the road. A thickness of the road is determined based on the point cloud data of the road. It is determined whether the internal parameter of the laser radar is precise based on the thickness of the road.

Abstract: Embodiments of the present disclosure disclose a method for calibrating a relative pose, a device, and a medium. The method includes: obtaining first point cloud data of a scene collected by the laser radar in an automatic driving mobile carrier and first pose data collected by the navigation positioning system in the automatic driving mobile carrier; and determining the relative pose between the laser radar and the navigation positioning system based on the first point cloud data, the first pose data, second point cloud data pre-collected by a laser scanner in the scene and second pose data pre-collected by a positioning device in the scene.

Abstract: Embodiments of the present disclosure disclose an object detection method, an object detection apparatus, a related device, a storage medium and a vehicle. The method includes the following. Scene point cloud data is inputted into a point cloud feature extraction model pretrained to obtain a point cloud descriptive feature of the scene. Scene image data is input into an image feature extraction model pretrained to obtain an image descriptive feature of the scene. The point cloud descriptive feature and the image descriptive feature of each target region in the scene are input into an object identifying model to obtain a detection result of an object included in each target region.

Abstract: Embodiments of the present disclosure provide a method, an apparatus, a device, and a medium for determining an angle of yaw, relating to a field of automatic driving. The method includes: obtaining, during a vehicle being driving straightly on a straight road, data of each obstacle in an environment located by the vehicle, the data of each obstacle being detected by a millimeter wave radar sensor located in the vehicle, at least one metal obstacle being provided on the straight road; recognizing the metal obstacle based on the data of each obstacle, and obtaining a metal obstacle line by fitting positions of the metal obstacle at different time points; and determining an angle between the metal obstacle line and a direction of a vehicle body as an angle of yaw between the millimeter wave radar sensor and the vehicle body.

Abstract: Embodiments of the present disclosure disclose a method and an apparatus for determining a relative pose, a device, and a medium. The method comprises: obtaining millimeter-wave data for obstacles in a scene collected by a millimeter-wave radar sensor in a vehicle and first data for the obstacles in the scene collected by a first sensor in the vehicle, wherein, at least one millimeter-wave radar signal booster is disposed in the scene as a target obstacle and the obstacles comprise the target obstacle; determining first obstacle data for the target obstacle in a millimeter-wave radar coordinate system and second obstacle data for the target obstacle in a first sensor coordinate system according to the millimeter-wave data and the first data for the obstacles in the scene; and determining the relative pose between the millimeter-wave radar sensor and the first sensor according to the first obstacle data and the second obstacle data.

Abstract: Embodiments of the present disclosure provide a method and an apparatus for clock synchronization, a device, a storage medium and a vehicle. The method includes: determining a first pose change of an image acquirer with respect to a reference coordinate system at different image acquisition time points; determining a second pose change of a calibration acquirer with respect to the reference coordinate system at different corrected image acquisition time points, in which, the different corrected image acquisition correction time points are determined according to the different image acquisition time points and each predetermined correction time variable; and performing a clock synchronization on the image acquirer and the calibration acquirer according to the first pose change of the image acquirer and the second pose change of the calibration acquirer corresponding to the each correction time variable.

Abstract: A method for determining a motion vector field is provided, comprising: determining an optical flow of a spatial point in a scene according to different frames of images acquired by an image acquisition unit; determining a movement velocity of the spatial point in a coordinate system of the image acquisition unit according to spatial point data; determining a first order derivative of a projection of the spatial point on an image plane according to the spatial point data; and determining a velocity vector of the spatial point according to intrinsic parameters, a rotation matrix in a global coordinate system, a movement velocity, and an angular velocity, of the image acquisition unit, as well as a pose, the optical flow, the movement velocity in the coordinate system of the image acquisition unit, and the first order derivative of the projection on the image plane, of the spatial point.

Abstract: The present disclosure provides a method and apparatus for calibration between a laser radar and a camera, a device and a storage medium, wherein the method comprises: respectively obtaining N stations of calibration data at N locations, each station of calibration data respectively comprising: image data collected by a camera and point cloud data collected by a laser radar, N being a positive integer; building an image pyramid for each station of image; performing a search for each layer of pyramid in a top-to-bottom order according to a preset initial calibration parameter and point cloud data, and determining an optimal calibration parameter according to search results. The solution of the present disclosure may be applied to achieve real-time online calibration.

Abstract: The present application discloses a method, an apparatus, a system, and a storage medium for calibrating an exterior parameter of an on-board camera, relating to the field of autonomous driving technologies. A specific implementation scheme of the method in the application is: preprocessing two frames of images of a former frame and a latter frame collected by the on-board camera; performing feature point matching on the two preprocessed frames of images to obtain matched feature points; determining a moving posture of the on-board camera according to the matched feature points; determining a conversion relationship between a vehicle coordinate system and an on-board camera coordinate system according to the moving posture, and obtaining the external parameter of the on-board camera relative to a vehicle body.

Abstract: Embodiments of the present disclosure provide a sensor calibration method, a sensor calibration device, a computer device, a storage medium, and a vehicle. The method includes: detecting surrounding objects in a travelling process of a vehicle; recognizing a static object from the surrounding objects; performing feature extraction on the static object by a camera and a lidar, respectively; and calibrating an extrinsic parameter of the camera and the lidar based on the extracted feature.

Abstract: Embodiments of the present disclosure provide a multi-sensor calibration method, a multi-sensor calibration device, a computer device, a medium and a vehicle. The method includes: acquiring data acquired by each of at least three sensors in a same time period in a traveling process of a vehicle; determining a trajectory of each of the at least three sensors according to the data acquired by each of at least three sensors; and performing a joint calibration on the at least three sensors by performing trajectory alignment on the trajectories of the at least three sensors.

Abstract: The present disclosure provides a method for calibrating relative parameters of a collector, an apparatus for calibrating relative parameters of a collector, a device and a storage medium. The technical solution of the present disclosure may determine target position and attitude information of a target image collector in a calibration coordinate system when each target image is collected, determine the first position information of a spatial point in a three-dimensional scene point cloud in the calibration coordinate system and the second position information a projection point of the spatial point in each target image, and determine a relative position and attitude value between the target image collector and the reference collector based on the target position and attitude information corresponding to each target image, the first position information and the second position information.

Abstract: Embodiments of the present disclosure disclose a method for calibrating a relative pose, a device, and a medium. The method includes: obtaining first point cloud data of a scene collected by the laser radar in an automatic driving mobile carrier and first pose data collected by the navigation positioning system in the automatic driving mobile carrier; and determining the relative pose between the laser radar and the navigation positioning system based on the first point cloud data, the first pose data, second point cloud data pre-collected by a laser scanner in the scene and second pose data pre-collected by a positioning device in the scene.

Abstract: Embodiments of the present disclosure provide a multi-sensor calibration method, a multi-sensor calibration device, a computer device, a medium and a vehicle. The method includes: acquiring data acquired by each of at least three sensors in a same time period in a traveling process of a vehicle; determining a trajectory of each of the at least three sensors according to the data acquired by each of at least three sensors; and performing a joint calibration on the at least three sensors by performing trajectory alignment on the trajectories of the at least three sensors.

Abstract: Embodiments of the present disclosure provide a sensor calibration method, a sensor calibration device, a computer device, a storage medium, and a vehicle. The method includes: detecting surrounding objects in a travelling process of a vehicle; recognizing a static object from the surrounding objects; performing feature extraction on the static object by a camera and a lidar, respectively; and calibrating an extrinsic parameter of the camera and the lidar based on the extracted feature.

Abstract: The present disclosure provides a method for calibrating relative parameters of a collector, an apparatus for calibrating relative parameters of a collector, a device and a storage medium. The technical solution of the present disclosure may determine target position and attitude information of a target image collector in a calibration coordinate system when each target image is collected, determine the first position information of a spatial point in a three-dimensional scene point cloud in the calibration coordinate system and the second position information a projection point of the spatial point in each target image, and determine a relative position and attitude value between the target image collector and the reference collector based on the target position and attitude information corresponding to each target image, the first position information and the second position information.

Abstract: Embodiments of the present disclosure provide a method and an apparatus for clock synchronization, a device, a storage medium and a vehicle. The method includes: determining a first pose change of an image acquirer with respect to a reference coordinate system at different image acquisition time points; determining a second pose change of a calibration acquirer with respect to the reference coordinate system at different corrected image acquisition time points, in which, the different corrected image acquisition correction time points are determined according to the different image acquisition time points and each predetermined correction time variable; and performing a clock synchronization on the image acquirer and the calibration acquirer according to the first pose change of the image acquirer and the second pose change of the calibration acquirer corresponding to the each correction time variable.

Abstract: Embodiments of the present disclosure disclose an object detection method, an object detection apparatus, a related device, a storage medium and a vehicle. The method includes the following. Scene point cloud data is inputted into a point cloud feature extraction model pretrained to obtain a point cloud descriptive feature of the scene. Scene image data is input into an image feature extraction model pretrained to obtain an image descriptive feature of the scene. The point cloud descriptive feature and the image descriptive feature of each target region in the scene are input into an object identifying model to obtain a detection result of an object included in each target region.