Abstract: A method for generating simulated point cloud data, a device, and a storage medium includes: acquiring at least one frame of point cloud data collected by a road collecting device in an actual environment without a dynamic obstacle as static scene point cloud data; setting, least one dynamic obstacle in a coordinate system matching the static scene point cloud data; simulating in the coordinate system, a plurality of simulated scanning lights emitted by a virtual scanner located at an origin of the coordinate system; updating the static scene point cloud data according to intersections of the plurality of simulated scanning lights and the at least one dynamic obstacle to obtain the simulated point cloud data comprising point cloud data of the dynamic obstacle; and at least one of adding a set noise to the simulated point cloud data, and, deleting point cloud data corresponding to the dynamic obstacle according to a set ratio.

Abstract: A method and system for simulating a distribution of obstacles are provided. The method includes: acquiring a plurality of point clouds of a plurality of frames, wherein each point cloud includes a plurality of original obstacles; acquiring real labeling data of an acquisition vehicle, and obtaining data of a simulation position of the acquisition vehicle based on the real labeling data and a movement rule of the acquisition vehicle; determining the number of obstacles to be simulated based on the data of the simulation position of the acquisition vehicle; selecting the determined number of obstacles to be simulated, from a range with the simulation position of the acquisition vehicle as a center, wherein the range is less than or equal to a maximum scanning range of the vehicle; and acquiring real labeling data of the selected obstacles, and obtaining a position distribution of the selected obstacles.

Abstract: Embodiments of an obstacle distribution simulation method, device and terminal based on a probability graph are provided. The method can include: acquiring a plurality of point clouds of a plurality of frames; acquiring real labeling data of an acquisition vehicle at vehicle labeled positions, and acquiring data of a simulation position of the acquisition vehicle; determining the number of obstacles to be simulated at a position to be simulated; extracting real labeling data of the obstacles, and constructing a labeling data set; dividing the labeling data set into a plurality of grids and calculating occurrence probabilities of the plurality of obstacles; selecting the determined number of obstacles to be simulated according to the occurrence probabilities; and acquiring a position distribution of the selected obstacles to be simulated for the position to be simulated based on the real labeling data of the selected obstacles to be simulated.

Abstract: A simulation data augmentation method, a simulation data augmentation device and a simulation data augmentation terminal are provided according to embodiments of the present application. The method includes: acquiring a point cloud based on a plurality of frames, wherein the point cloud includes a plurality of obstacles labeled with real labeling data; dividing the point cloud into a plurality of preset regions, wherein each of the preset regions includes at least one obstacle; and adjusting the obstacle based on the real labeling data of the obstacle in the preset regions to obtain simulation data.

Abstract: A data augmentation method, device are provided according to embodiments of the present application. The method includes: acquiring a point cloud of a frame, the point cloud comprising a plurality of original obstacles; obtaining a plurality of position voids by removing the original obstacles from the point cloud, and filling the position voids to obtain a real background of the point cloud; arranging a plurality of new obstacles labeled by labeling data, in the real background of the point cloud; and adjusting the new obstacles based on the labeling data of the new obstacles to obtain layout data of the new obstacles. The amount of real data is increased, and a diversity of the real data is improved.

Abstract: A method for generating simulated point cloud data, a device, and a storage medium includes: acquiring at least one frame of point cloud data collected by a road collecting device in an actual environment without a dynamic obstacle as static scene point cloud data; setting, least one dynamic obstacle in a coordinate system matching the static scene point cloud data; simulating in the coordinate system, a plurality of simulated scanning lights emitted by a virtual scanner located at an origin of the coordinate system; updating the static scene point cloud data according to intersections of the plurality of simulated scanning lights and the at least one dynamic obstacle to obtain the simulated point cloud data comprising point cloud data of the dynamic obstacle; and at least one of adding a set noise to the simulated point cloud data, and, deleting point cloud data corresponding to the dynamic obstacle according to a set ratio.

Abstract: A method and apparatus for identifying laser point cloud data of an autonomous vehicle provided with a lidar. A specific implementation of the method includes: acquiring current pose information of the autonomous vehicle in a world coordinate system in response to receiving a latest frame of laser point cloud data collected by the lidar; acquiring from the cache, based on the current pose information, N×N map blocks centered on map blocks corresponding to the current pose information in a preset three-dimensional grid map and pre-loaded into a cache and are; and executing, for each laser point data in the received laser point cloud data, the laser point data identification operations. The implementation realizes identifying whether each laser point data is a static laser point, and can improve the accuracy rate of identifying a laser point data obstacle.

Abstract: Embodiments of the present disclosure are directed to a method for generating simulated point cloud data, a device, and a storage medium. The method includes: acquiring at least one frame of point cloud data collected by a road collecting device in an actual environment without a dynamic obstacle as static scene point cloud data; setting, according to set position association information, at least one dynamic obstacle in a coordinate system matching the static scene point cloud data; simulating in the coordinate system, according to the static scene point cloud data, a plurality of simulated scanning lights emitted by a virtual scanner located at an origin of the coordinate system; and updating the static scene point cloud data according to intersections of the plurality of simulated scanning lights and the at least one dynamic obstacle to obtain the simulated point cloud data comprising point cloud data of the dynamic obstacle.

Abstract: A method, an apparatus, a device, and a medium for calibrating a posture of a moving obstacle are provided. The method includes: obtaining a 3D map, the 3D map including first static obstacles; selecting a target frame of data, the target frame of data including second static obstacles and one or more moving obstacles; determining posture information of each of the one or more moving obstacles in a coordinate system of the 3D map; registering the target frame of data with the 3D map; determining posture offset information of the target frame of data in the coordinate system according to a registration result; calibrating the posture information of each of the one or more moving obstacles according to the posture offset information; and adding each of the one or more moving obstacles after the calibrating into the 3D map.

Abstract: The present disclosure provides a method and a device for generating a 3D scene map, a related apparatus and a storage medium. The method includes the following. At least two frames of point cloud data collected by a collection device is obtained. Data registration is performed on the at least two frames of point cloud data. First type of point cloud data corresponding to a movable obstacle is deleted from each frame of point cloud data and each frame of point cloud data is merged to obtain an initial scene map. Second type of point cloud data corresponding to a regularly shaped object is replaced with model data of a geometry model matching with the regularly object for the initial scene map to obtain the 3D scene map.

Abstract: An obstacle distribution simulation method, device and terminal based on multiple models. The method can include: acquiring a point cloud, the point cloud including a plurality of obstacles labeled with real labeling data; extracting the real labeling data of the obstacles, and training a plurality of neural network models based on the real labeling data of the obstacles; extracting unlabeled data in the point cloud, inputting the unlabeled data into the neural network models, and outputting a plurality of prediction results. The plurality of prediction results can include a plurality of simulated obstacles with attribute data; selecting at least one simulated obstacle based on the plurality of prediction results; and inputting the attribute data of the selected simulated obstacle into the neural network models to obtain position coordinates of the simulated obstacle, and further obtain a position distribution of the simulated obstacle.

Abstract: A method and system for simulating a distribution of obstacles are provided. The method includes: acquiring a plurality of point clouds of a plurality of frames, wherein each point cloud includes a plurality of original obstacles; acquiring real labeling data of an acquisition vehicle, and obtaining data of a simulation position of the acquisition vehicle based on the real labeling data and a movement rule of the acquisition vehicle; determining the number of obstacles to be simulated based on the data of the simulation position of the acquisition vehicle; selecting the determined number of obstacles to be simulated, from a range with the simulation position of the acquisition vehicle as a center, wherein the range is less than or equal to a maximum scanning range of the vehicle; and acquiring real labeling data of the selected obstacles, and obtaining a position distribution of the selected obstacles.

Abstract: A data augmentation method, device are provided according to embodiments of the present application. The method includes: acquiring a point cloud of a frame, the point cloud comprising a plurality of original obstacles; obtaining a plurality of position voids by removing the original obstacles from the point cloud, and filling the position voids to obtain a real background of the point cloud; arranging a plurality of new obstacles labeled by labeling data, in the real background of the point cloud; and adjusting the new obstacles based on the labeling data of the new obstacles to obtain layout data of the new obstacles. The amount of real data is increased, and a diversity of the real data is improved.

Abstract: Embodiments of an obstacle distribution simulation method, device and terminal based on a probability graph are provided. The method can include: acquiring a plurality of point clouds of a plurality of frames; acquiring real labeling data of an acquisition vehicle at vehicle labeled positions, and acquiring data of a simulation position of the acquisition vehicle; determining the number of obstacles to be simulated at a position to be simulated; extracting real labeling data of the obstacles, and constructing a labeling data set; dividing the labeling data set into a plurality of grids and calculating occurrence probabilities of the plurality of obstacles; selecting the determined number of obstacles to be simulated according to the occurrence probabilities; and acquiring a position distribution of the selected obstacles to be simulated for the position to be simulated based on the real labeling data of the selected obstacles to be simulated.

Abstract: An obstacle distribution simulation method, device and terminal based on multiple models. The method can include: acquiring a point cloud, the point cloud including a plurality of obstacles labeled with real labeling data; extracting the real labeling data of the obstacles, and training a plurality of neural network models based on the real labeling data of the obstacles; extracting unlabeled data in the point cloud, inputting the unlabeled data into the neural network models, and outputting a plurality of prediction results. The plurality of prediction results can include a plurality of simulated obstacles with attribute data; selecting at least one simulated obstacle based on the plurality of prediction results; and inputting the attribute data of the selected simulated obstacle into the neural network models to obtain position coordinates of the simulated obstacle, and further obtain a position distribution of the simulated obstacle.

Abstract: A simulation data augmentation method, a simulation data augmentation device and a simulation data augmentation terminal are provided according to embodiments of the present application. The method includes: acquiring a point cloud based on a plurality of frames, wherein the point cloud includes a plurality of obstacles labeled with real labeling data; dividing the point cloud into a plurality of preset regions, wherein each of the preset regions includes at least one obstacle; and adjusting the obstacle based on the real labeling data of the obstacle in the preset regions to obtain simulation data.

Abstract: A method, an apparatus, a device, and a medium for calibrating a posture of a moving obstacle are provided. The method includes: obtaining a 3D map, the 3D map including first static obstacles; selecting a target frame of data, the target frame of data including second static obstacles and one or more moving obstacles; determining posture information of each of the one or more moving obstacles in a coordinate system of the 3D map; registering the target frame of data with the 3D map; determining posture offset information of the target frame of data in the coordinate system according to a registration result; calibrating the posture information of each of the one or more moving obstacles according to the posture offset information; and adding each of the one or more moving obstacles after the calibrating into the 3D map.

Abstract: Embodiments of the present disclosure are directed to a method for generating simulated point cloud data, a device, and a storage medium. The method includes: acquiring at least one frame of point cloud data collected by a road collecting device in an actual environment without a dynamic obstacle as static scene point cloud data; setting, according to set position association information, at least one dynamic obstacle in a coordinate system matching the static scene point cloud data; simulating in the coordinate system, according to the static scene point cloud data, a plurality of simulated scanning lights emitted by a virtual scanner located at an origin of the coordinate system; and updating the static scene point cloud data according to intersections of the plurality of simulated scanning lights and the at least one dynamic obstacle to obtain the simulated point cloud data comprising point cloud data of the dynamic obstacle.

Abstract: The present disclosure provides a method and a device for generating a 3D scene map, a related apparatus and a storage medium. The method includes the following. At least two frames of point cloud data collected by a collection device is obtained. Data registration is performed on the at least two frames of point cloud data. First type of point cloud data corresponding to a movable obstacle is deleted from each frame of point cloud data and each frame of point cloud data is merged to obtain an initial scene map. Second type of point cloud data corresponding to a regularly shaped object is replaced with model data of a geometry model matching with the regularly object for the initial scene map to obtain the 3D scene map.

Abstract: The present disclosure discloses a method and apparatus for identifying laser point cloud data of an autonomous vehicle provided with a lidar. A specific implementation of the method comprises: acquiring current pose information of the autonomous vehicle in a world coordinate system in response to receiving a latest frame of laser point cloud data collected by the lidar; acquiring from the cache, based on the current pose information, N×N map blocks centered on map blocks corresponding to the current pose information in a preset three-dimensional grid map and pre-loaded into a cache and are; and executing, for each laser point data in the received laser point cloud data, the laser point data identification operations. The implementation realizes identifying whether each laser point data is a static laser point, and can improve the accuracy rate of identifying a laser point data obstacle.