Abstract: The present disclosure provides a vehicle positioning method, an apparatus and an autonomous driving vehicle, relating to autonomous driving in the technical field of artificial intelligence, which can be applied to high-definition positioning of the autonomous driving vehicle, the method including: if there is no high-definition map in a vehicle, acquiring intermediate pose information of the vehicle based on a global navigation satellite system and/or an inertial measurement unit in the vehicle, and determining the intermediate pose information as global positioning information; acquiring local positioning information; performing fusion processing to the global pose information and the local pose information to obtain fused pose information; performing compensation processing to the fused pose information according to the global attitude angle information and the local attitude angle information to obtain a position of the vehicle.

Abstract: The present disclosure provides a method, an apparatus, a computer device and a computer-readable storage medium for positioning, and relates to the field of autonomous driving. The method obtains point cloud data collected by a LiDAR on a device at a current time; determines, based on the point cloud data and a global map built in a global coordinate system, global positioning information of the device in the global coordinate system at the current time; and determine, based on the point cloud data and a local map built in a local coordinate system, local positioning information of the device in the local coordinate system at the current time. A positioning result of the device at the current time is determined based on at least the global positioning information and the local positioning information. Techniques of the present disclosure can provide an effective and stable positioning service.

Abstract: A method, an electronic device and a readable storage medium for point cloud data processing, which may be used for autonomous driving, are disclosed. The feature vectors of respective points in the first point cloud data and second point cloud data are pre-learned, and thus the feature vectors of the first key points may be determined directly based on the learnt second feature vectors of respective first neighboring points of the respective first key points in the first point cloud data, and the feature vectors of the candidate key points may be determined directly based on the learnt third feature vectors of the respective second neighboring points of respective candidate key points in the second point cloud data corresponding to the first key points.

Abstract: A method is provided. The method includes: obtaining global position and orientation information of a first trajectory point; determining at least one pair of second trajectory points based on the global position and orientation information; obtaining a relative position and orientation estimation information and a relative position and orientation measurement information of each pair of second trajectory points; and calculating a local rotation offset at the first trajectory point based on the relative position and orientation estimation information and the relative position and orientation measurement information of each pair of second trajectory points in the at least one pair of second trajectory points to correct the global position and orientation information.

Abstract: The present disclosure provides a data processing method, an apparatus, an electronic device and a medium, which relates to the technical fields of autonomous driving, electronic maps, deep learning, image processing, and the like. The method includes: a computing device inputs a reference image and a captured image into a feature extraction model; obtain, a set of reference descriptors based on the first descriptor map; determine a plurality of sets of training descriptors; obtain a predicted pose of the vehicle by inputting the plurality of training poses and a plurality of similarities into a pose prediction model; and train the feature extraction model and the pose prediction model. When applied to a vehicle localization system, the trained feature extraction model and pose prediction model according to some embodiments of the present disclosure can improve accuracy and robustness of vehicle localization, thereby boosting the performance of the vehicle localization system.

Abstract: The present disclosure provides a method, an apparatus, a computer device and a computer-readable storage medium for positioning, and relates to the field of autonomous driving. The method obtains inertial measurement data of a device to be positioned at a current time and point cloud data collected by a LiDAR on the device at the current time; determines, by integrating the inertial measurement data, inertial positioning information of the device in an inertial coordinate system at the current time; and determines, based on the inertial positioning information, the point cloud data and at least one local map built in a local coordinate system, a positioning result of the device in the local coordinate system at the current time. Techniques of the present disclosure can provide an effective and stable local positioning result.

Abstract: A positioning method includes acquiring an image of an area where a target object is located at a first time instant and multiple frames of point cloud data of an area where the target object is located at multiple time instants, wherein the multiple frames of point cloud data include first point cloud data of the area where the target object is located at the first time instant. The method also includes determining a point cloud map according to the multiple frames of point cloud data and acquiring a target feature vector according to the first point cloud data, the point cloud map and the image. The method further includes determining a positioning result of the target object according to the target feature vector.

Abstract: A positioning method based on a lane line and a feature point, an electronic device, and a storage medium, which relate to fields of computer, automatic driving, intelligent transportation, computer vision. The method may include: determining first real-time measurement residuals according to first sensor information of a movable object detected, the first real-time measurement residuals including a first inertial measurement unit measurement residual, a first lane line measurement residual and a first non-lane line measurement residual; updating a state vector of the movable object according to a kinematic model of an inertial measurement unit and the first real-time measurement residuals; and determining a pose of the movable object at a time instant corresponding to the updated state vector according to a pose vector in the updated state vector.

Abstract: A method of processing data for an autonomous vehicle, an electronic device, a storage medium, and an autonomous vehicle are provided. The method includes: acquiring sensor data for the autonomous vehicle, wherein the sensor data includes inertial measurement data, LiDAR data, and visual image data; determining a first constraint factor for the inertial measurement data according to the inertial measurement data and the visual image data; determining a second constraint factor for the LiDAR data according to the inertial measurement data and the LiDAR data; determining a third constraint factor for the visual image data according to the inertial measurement data, the visual image data and the LiDAR data; and processing the sensor data based on the first constraint factor, the second constraint factor and the third constraint factor, so as to obtain positioning data for positioning the autonomous vehicle.

Abstract: A method for fusing road data to generate a map, includes: determining benchmark road data and at least one road data to be fused in a target road area; establishing, successively for each road data to be fused, a first road element association relationship between the first sub road data and the benchmark road data; establishing a second road element association relationship between the second sub road data and the benchmark road data according to the first road element association relationship; and fusing the benchmark road data and the road data to be fused according to the above association relationships to update the benchmark road data.

Abstract: Exemplary positioning method and apparatus, a device, a system, a medium and a self-driving vehicle are provided. The positioning method includes determining, according to the current frame of point cloud data collected by a to-be-positioned terminal in a travelling environment, the current key point in the current frame of point cloud data and a point cloud distribution feature of the current key point; selecting, according to point cloud distribution features associated with reference key points in a global positioning map and the point cloud distribution feature of the current key point, a target key point matching the current key point from the reference key points; and determining, according to reference pose data associated with the target key point, the current pose data of the to-be-positioned terminal.

Abstract: In one embodiment, a method for extracting point cloud features for use in localizing an autonomous driving vehicle (ADV) includes selecting a first set of keypoints from an online point cloud, the online point cloud generated by a LiDAR device on the ADV for a predicted pose of the ADV; and extracting a first set of feature descriptors from the first set of keypoints using a feature learning neural network running on the ADV, The method further includes locating a second set of keypoints on a pre-built point cloud map, each keypoint of the second set of keypoints corresponding to a keypoint of the first set of keypoint; extracting a second set of feature descriptors from the pre-built point cloud map; and estimating a position and orientation of the ADV based on the first set of feature descriptors, the second set of feature descriptors, and a predicted pose of the ADV.

Abstract: The disclosure provides a method for detecting an obstacle, an electronic device, and a storage medium, relates to the field of artificial intelligence, and in particular, to the field of automatic driving. A specific implementation solution includes: acquiring point cloud data collected by a lidar sensor during detection of an obstacle; converting the point cloud data into an obstacle feature map, where the obstacle feature map includes a to-be-detected image feature, and the to-be-detected image feature includes an obstacle feature; performing feature extraction on the to-be-detected image feature in the obstacle feature map to obtain the obstacle feature, increasing a weight value in the obstacle feature map of the obstacle feature during the feature extraction, and monitoring the feature extraction based on monitoring data generated by point cloud distribution data corresponding to the point cloud data; and determining a target obstacle according to the obstacle feature.

Abstract: The present disclosure provides a method and device for predicting a motion track of an obstacle and an autonomous vehicle, and relates to the technical field of autonomous driving, so as to at least solve the technical problem of low prediction precision of a motion track of an obstacle in an interaction scene. A specific implementation solution includes: environment information in a target scene, historical state information of a target obstacle and track planning information of a target vehicle are obtained, and the target obstacle is a potential interaction object of the target vehicle; and a motion track of the target obstacle is predicted based on the environment information, the historical state information and the track planning information.

Abstract: A high-definition map creation method includes: obtaining point cloud data collected with respect to a target region, the point cloud data including K frames of point clouds and an initial pose of each frame of point cloud, K being an integer greater than 1; associating the K frames of point clouds with each other in accordance with the initial pose to obtain a first point cloud relation graph of the K frames of point clouds; performing point cloud registration on the K frames of point clouds in accordance with the first point cloud relation graph and the initial pose to obtain a target relative pose of each frame of point cloud in the K frames of point clouds; and splicing the K frames of point clouds in accordance with the target relative pose to obtain a point cloud map of the target region.

Abstract: In one embodiment, a method for solution inference using neural networks in LiDAR localization includes constructing a cost volume in a solution space for a predicted pose of an autonomous driving vehicle (ADV), the cost volume including a number of sub volumes, each sub volume representing a matching cost between a keypoint from an online point cloud and a corresponding keypoint on a pre-built point cloud map. The method further includes regularizing the cost volume using convention neural networks (CNNs) to refine the matching costs; and inferring, from the regularized cost volume, an optimal offset of the predicted pose. The optimal offset can be used to determine a location of the ADV.

Abstract: The present disclosure provides a point cloud data processing method, apparatus, electronic device and computer readable storage medium, which relates to computer vision technology and may be used for autonomous driving.

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: The present disclosure provides a method, an apparatus, a computing device and a computer readable storage medium for detecting an environmental change, and relates to the field of autonomous driving. The method obtains a global map for an area and a first local map built in real time for a sub-area in the area; and determines an environmental change in the first sub-area by comparing the first local map and the global map and determining a first probability of the environmental change. Techniques of the present disclosure can automatically detect environmental changes that affect the positioning of autonomous driving, thereby facilitating the updating of positioning maps.

Abstract: The present disclosure provides a vehicle positioning method, an apparatus and an autonomous driving vehicle, relating to autonomous driving in the technical field of artificial intelligence, which can be applied to high-definition positioning of the autonomous driving vehicle, the method including: if there is no high-definition map in a vehicle, acquiring intermediate pose information of the vehicle based on a global navigation satellite system and/or an inertial measurement unit in the vehicle, and determining the intermediate pose information as global positioning information; acquiring local positioning information; performing fusion processing to the global pose information and the local pose information to obtain fused pose information; performing compensation processing to the fused pose information according to the global attitude angle information and the local attitude angle information to obtain a position of the vehicle.