Beijing Baidu Netcom Science Technology Co., Ltd

Apollo Intelligent Driving Technology (beijing) Co., Ltd.

Baidu USA LLC

Beijing Baidu Netcom Science and Technology Co., Ltd.

Baidu Online Network Technology (beijing) Co., Ltd.

Wendong Ding

Shiyu Song

Guowei Wan

Li Yu

Pengfei Yuan

Shenhua Hou

Hang Gao

Liang Peng

Runxin He

Wei Gao

Fan Yi

Yucheng Zhang

Xiaofei Rui

Gang Wang

Yingying Qin

Yangyang Dai

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.

High-definition map creation method and device, and electronic device

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.

Method of processing data for autonomous vehicle, electronic device, storage medium and autonomous vehicle

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.

Method, apparatus, computing device and computer-readable storage medium for positioning

Embodiments of the present application disclose a positioning method and apparatus, an autonomous driving vehicle, an electronic device and a storage medium, relating to the field of autonomous driving technologies, comprising: collecting first pose information measured by an inertial measurement unit within a preset time period, and collecting second pose information measured by a wheel tachometer within the time period; generating positioning information according to the first pose information, the second pose information and the adjacent frame images; controlling driving of the autonomous driving vehicle according to the positioning information. The positioning information is estimated by combining the first pose information and the second pose information corresponding to the inertial measurement unit and the wheel tachometer respectively. Compared with the camera, the inertial measurement unit and the wheel tachometer are not prone to be interfered by the external environment.

Control of autonomous vehicle based on fusion of pose information and visual data

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.

In one embodiment, a system and method for partitioning a region for point cloud registration of LIDAR poses of an autonomous driving vehicle (ADV) using a regional iterative closest point (ICP) algorithm is disclosed. The method determines the frame pair size of one or more pairs of related LIDAR poses of a region of an HD map to be constructed. If the frame pair size is greater than a threshold, the region is further divided into multiple clusters. The method may perform the ICP algorithm for each cluster. Inside a cluster, the ICP algorithm focuses on a partial subset of the decision variables and assumes the rest of the decision variables are fixed. To construct the HD map, the method may determine if the results of the ICP algorithms from the clusters converge. If the solutions converge, a solution to the point cloud registration for the region is found.

Coordinate gradient method for point cloud registration for autonomous vehicles

The present application discloses a high-precision mapping method and device, which relates to the field of autonomous driving. A specific implementation includes: acquiring global initial poses of multiple point clouds, where the point clouds are point clouds of a location for which a map is to be built and are collected by a lidar using a multi-circle collection mode; dividing the multiple point clouds into multiple spatial submap graphs according to a spatial distribution relationship of the multiple point clouds; optimizing, for each spatial submap graph, global initial poses of point clouds belonging to the spatial submap graph to acquire global poses of the point clouds in each spatial submap graph; and stitching the multiple spatial submap graphs together according to global poses of the point clouds in the multiple spatial submap graphs to acquire a base graph of the map to be built.

High-precision mapping method and device

In one embodiment, a system and method for point cloud registration of LIDAR poses of an autonomous driving vehicle (ADV) is disclosed. The method selects poses of the point clouds that possess higher confidence level during the data capture phase as fixed anchor poses. The fixed anchor points are used to estimate and optimize the poses of non-anchor poses during point cloud registration. The method may partition the points clouds into blocks to perform the ICP algorithm for each block in parallel by minimizing the cost function of the bundle adjustment equation updated with a regularity term. The regularity term may measure the difference between current estimates of the poses and previous or the initial estimates. The method may also minimize the bundle adjustment equation updated with a regularity term when solving the pose graph problem to merge the optimized poses from the blocks to make connections between the blocks.

Updated point cloud registration pipeline based on ADMM algorithm for autonomous vehicles

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.

Method, apparatus, device and medium for detecting environmental change

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.

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Beijing, China

Wendong Ding