Abstract: Disclosed embodiments include a method and a device for generating position information of a target object. In some embodiments, the method comprises: obtaining target point cloud data of the target object, acquired by a target scanner at a target position, and position information of the target scanner, and obtaining reference point cloud data of the target object, acquired by a reference scanner at a reference position, and position information and an Euler angle of the reference scanner; obtaining an Euler angle of the target scanner based on the target point cloud data, the reference point cloud data, and the Euler angle of the reference scanner; and generating the position information of the target object based on the target point cloud data, the position information and the Euler angle of the target scanner, the reference point cloud data, and the position information and the Euler angle of the reference scanner.

Abstract: A specific implementation of the method includes: constructing a reflectance map based on a position and an Euler angle, obtained through a global pose optimization and used for constructing a reflectance map, of a center of a laser radar corresponding to each frame laser point cloud used for constructing the reflectance map. This implementation implements the level-by-level pose optimization of key frame laser point clouds, sample frame laser point clouds, regular frame laser point clouds selected from laser point clouds used for constructing a reflectance map, to obtain an accurate position and Euler angle, used for constructing the reflectance map, of a center of the laser radar corresponding to each frame laser point cloud used for constructing the reflectance map, so that accurate coordinates of laser points in each frame laser point cloud used for constructing the reflectance map in a world coordinate system can be obtained.

Abstract: The present application discloses a method and apparatus for positioning a vehicle. An implementation of the method comprises: obtaining laser point cloud data of a laser point cloud collected by a laser radar on a vehicle, and obtaining an initial pose of a center point of the laser radar; calculating a matching probability between projection data corresponding to each sampling pose and map data of a reflected value map respectively; and calculating an optimal pose based on the matching probability between the projection data corresponding to the each sampling pose and the map data of the reflected value map, and determining a position of the vehicle based on the optimal pose.

Abstract: Embodiments of the present disclosure provide a method and a device for positioning a vehicle, a device, and a computer readable storage medium. The method includes: determining first edge information of a vehicle from an image related to the vehicle and acquired by a sensing device; determining a contour model corresponding to the vehicle; determining second edge information that matches the first edge information from an edge information set associated with the contour model, in which each edge information in the edge information set corresponds to a position of the contour model relative to the sensing device; and determining a position of the vehicle relative to the sensing device based on a position corresponding to the second edge information.

Abstract: Embodiments of the present disclosure disclose a method and apparatus for positioning an autonomous vehicle. The method includes: matching a current point cloud projected image of a first resolution with a map of the first resolution to generate a first histogram filter based on the matching result; determining at least two first response areas in the first histogram filter based on a probability value of an element in the first histogram filter; generating a second histogram filter based on a result of matching a current point cloud projected image of a second resolution with a map of the second resolution and the at least two first response areas, the first resolution being less than the second resolution; and calculating a weighted average of probability values of target elements in the second histogram filter to determine a positioning result of the autonomous vehicle in the map of the second resolution.

Abstract: A method and an apparatus for processing information are provided. A method may include: sending a preset data acquisition request to a target data transmission end included in at least two preset data transmission ends; receiving feedback information from the target data transmission end, and determining whether the feedback information includes the acquired data; determining, in response to determining that the feedback information does not include the acquired data, whether the feedback information includes first fault information for indicating that the target data transmission end malfunctions; and selecting, in response to determining that the feedback information includes the first fault information, a data transmission end other than the target data transmission end from the at least two data transmission ends as a new target data transmission end.

Abstract: Embodiments of the present disclosure provide a method and a device for generating an electronic map, an electronic device, a computer readable storage medium, and an acquisition entity. The method includes: obtaining a first point cloud sequence and a second point cloud sequence for a preset region; generating a first grid map for the first point cloud sequence and a second grid map for the second point cloud sequence, wherein a grid in each of the first grid map and the second grid map at least comprises reflection value information of a point cloud; and optimizing the first point cloud sequence based on the first grid map and the second grid map.

Abstract: Embodiments of the present disclosure provide a method and a device for acquiring a map, a device, and a computer readable storage medium. The method includes: acquiring a reference map of an acquisition area; dividing the acquisition area into a plurality of sub-areas based on the reference map; generating a plurality of acquisition tasks corresponding to the plurality of sub-areas, wherein each of the plurality of acquisition tasks is configured to acquire a map of each of the plurality of sub-areas; and assigning each of the plurality of acquisition tasks to an acquisition entity, to enable the acquisition entity to acquire the map of each of the plurality of sub-areas.

Abstract: The present disclosure provides a method and a device for positioning, an apparatus and a computer readable storage medium. The method includes acquiring a visual feature map of a geographic area located by a movable object. The visual feature map includes a first set of visual features. The method further includes acquiring at least one image captured by a camera coupled to the movable object during a motion of the movable object within the geographic area. The method further includes extracting a second set of visual features corresponding to the first set of visual features from the at least one image. In addition, the method further includes determining at least one pose of the movable object during the motion at least based on the visual feature map and the second set of visual features.

Abstract: According to exemplary embodiments of the present disclosure, a method and an apparatus for extracting a lane line, a device, a computer readable-storage medium and a collection entity are provided. The method includes: obtaining a first group of lane lines of a road based on a first image generated from a point cloud collected by a laser radar; obtaining a second group of lane lines of the road based on a second image collected by a camera; and determining a lane line set of the road based on the first group of lane lines and the second group of lane lines. With embodiments of the present disclosure, the lane line extracted from the point cloud collected by the laser radar and the lane line extracted from the camera image are merged, thereby determining a final lane line, such that the extracted lane line is more accurate and more comprehensive.

Abstract: Embodiments of the present disclosure relate to a method, apparatus, device and computer readable storage medium for reconstructing a three-dimensional scene. The method for reconstructing a three-dimensional scene includes acquiring a point cloud data frame set for a three-dimensional scene, point cloud data frames in the point cloud data frame set respectively having a pose parameter. The method further comprises determining a subset corresponding to a part of the three-dimensional scene from the point cloud data frame set. The method further comprises adjusting a pose parameter of a point cloud data frame in the subset to obtain an adjusted subset, the adjusted subset including at least two point cloud data frames having matching overlapping parts. The method further comprises updating the point cloud data frame set using the adjusted subset. In this way, distributed processing on a large amount of point cloud data may be realized.

Abstract: A stationary object detecting method, a stationary object apparatus, and an electronic device are disclosed in embodiments of the present disclosure, the method includes: obtaining point cloud data of a scene and feature information of each of data points in the point cloud data; performing a triangulation network connection on each of the data points in the point cloud data to generate a triangular network model, and taking a picture of the triangular network model using a preset camera to obtain an image; obtaining a first data point corresponding to each pixel point in the image, and obtaining a feature map of the point cloud data according to feature information of each first data point; inputting the feature map into a classification model to obtain data points corresponding to the stationary object in the point cloud data.

Abstract: The present disclosure provides a method, an apparatus, and a device for switching GNSS reference station and a storage medium. The method of the present disclosure, by that in the process of moving of a positioning device, the positioning device transmits current position of the positioning device itself information to a base station server in real time, and the base station server determines a base station closest to the positioning device according to the current position information of the positioning device and position information of each base station; and the base station server switches a reference station of the positioning device to the base station closest to the positioning device, ensures that in the process of moving of the positioning device, the reference station of the positioning device is automatically switched according to a real-time position of the positioning device.

Abstract: The present disclosure provides a method and an apparatus for generating a high precision map, and a storage medium for generating a high precision map. The method includes: performing a point cloud splicing process on target point cloud data to obtain a lidar pose corresponding to the target point cloud data; projecting the target point cloud data into a preset two-dimensional area based on the lidar pose to generate a map based on a reflection value and a height value; performing a self-positioning verification on the map based on the reflection value and the height value using the target point cloud data; and integrating, if a result of the self-positioning verification satisfies a preset condition, the map based on the reflection value and the height value into a reference map to generate the high precision map. The present disclosure can obtain a high precision map with a wider application range.

Abstract: The present disclosure discloses a method, apparatus and server for constructing a map. An embodiment of the method comprises: building a point cloud data frame sequence using point cloud data frames acquired in real time; extracting morphological data from the point cloud data frames; establishing a spatial position relation between the morphological data of two adjacent point cloud data frames in the point cloud data frame sequence; determining a reference spatial position relation corresponding to a stationary object based on the spatial position relation, and constructing a map with the reference spatial position relation. The present embodiment realizes constructing a map with the point cloud data.

Abstract: The present disclosure discloses a method and apparatus for acquiring information. A specific embodiment of the method includes: collecting characteristic information of a road object, the characteristic information comprising: a position, in a world coordinate system, of laser points corresponding to the road object in a laser point cloud collected in an area where the road object is located, and a position in a reflectance map corresponding to a position of a control point of the road object in the world coordinate system; and determining whether a preset accuracy is acceptable based on a match between the characteristic information of the road object and a preset characteristic information of the road object, the preset accuracy comprising: a splicing accuracy of the spliced laser point cloud, an accuracy of the reflectance map, and an accuracy of the high-precision map.

Abstract: The present disclosure provides an integrated positioning method and system. The method comprises: receiving IMU data, and solving for the IMU data in a first Kalman filter to obtain a system state variable of an autonomous vehicle; receiving and buffering measurement data, and regarding the measurement data as current measurement data; performing measurement updating: obtaining the system state variable at a measurement time of current measurement data, and solving in the second Kalman filter to obtain an updated system state variable; if measurement data after the measurement time of the measurement data is already buffered, regarding the already-buffered measurement data after the measurement time as current measurement data, and performing the measurement update. The present disclosure can solve the problem about measurement information disorder caused by inconsistency of time delay in solving between different sensors, and improve real time and accuracy of the positioning.

Abstract: The present disclosure provides a laser point cloud positioning method and system. The method comprises: converting laser point cloud reflection value data and height value data matched with a current location of an autonomous vehicle into laser point cloud projection data in a ground plane; assigning a weight for a reflection value matching probability and a height value matching probability of the laser point cloud projection data and a laser point cloud two-dimensional grid map, and determining a matching probability of the laser point cloud projection data and the laser point cloud two-dimensional grid map; determining a location of the autonomous vehicle in the laser point cloud two-dimensional grid map based on a matching probability of the laser point cloud projection data and the laser point cloud two-dimensional grid map.

Abstract: A specific implementation of the method includes: constructing a reflectance map based on a position and an Euler angle, obtained through a global optimization and used for constructing a reflectance map, of a center of a laser radar corresponding to each frame laser point cloud used for constructing the reflectance map collected in each collection region. This implementation implements the level-by-level pose optimization of laser point clouds used for constructing a reflectance map that are collected in each collection region in an excessively large region, to obtain an accurate position and Euler angle, used for constructing the reflectance map, of a laser radar center corresponding to each frame laser point cloud used for constructing the reflectance map, so that accurate coordinates of laser points in each frame laser point cloud used for constructing the reflectance map in a world coordinate system may be obtained.

Abstract: A specific implementation of the method comprises: constructing a reflectance map based on a position and an Euler angle, obtained through a global pose optimization and used for constructing a reflectance map, of a center of a laser radar corresponding to each frame laser point cloud used for constructing the reflectance map. This implementation implements the level-by-level pose optimization of key frame laser point clouds, sample frame laser point clouds, regular frame laser point clouds selected from laser point clouds used for constructing a reflectance map, to obtain an accurate position and Euler angle, used for constructing the reflectance map, of a center of the laser radar corresponding to each frame laser point cloud used for constructing the reflectance map, so that accurate coordinates of laser points in each frame laser point cloud used for constructing the reflectance map in a world coordinate system can be obtained.