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

Baidu USA LLC

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

Baidu.com Times Technology (beijing) Co. Ltd.

Beijing Baidu Netcom Science Technology Co., Ltd

Beijing Baidu Netcom Science and Technology Co., Ltd.

Nec Laboratories America, Inc.

Nec Corporation

Baidu Online Network Technology Co., Ltd.

Shiyu Song

Li Yu

Guowei Wan

Fangfang Dong

Runxin He

Pengfei Yuan

Shenhua Hou

Wendong Ding

Weixin Lu

Yao Zhou

Manmohan Chandraker

Renlan Cai

Baoqiang Xu

Shichun Yi

Yong Xiao

Jiangtao Hu

Jinghao Miao

Shu Jiang

Jinyun Zhou

Fan Yang

Cheng Fang Wang

Qi Luo

Yu Wang

Jiaxuan Xu

Liang Peng

Hang Gao

Xiaolong Yang

Wenbo Li

Hao Jia Li

Xiaoxing Zhu

Tianlei Zhang

Wenjie Liu

Zhijie Wang

Xiaotao Li

Shengpan Xu

Yang Liu

Hao Wang

Liang Wang

Yifei Jiang

Jiaming Tao

Shuang Zhang

Jie Huang

Yuanqing Lin

Hao Li

Lixia Shen

Xiaoyu Wang

He Yan

Feilong Yan

Fan Yi

Bosheng Wang

Xiaofei Rui

Xiangyu Fu

Ruijie Hou

Jingsen Zheng

Weilin Peng

Hailong Tian

Gang Wang

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

The present application discloses a positioning method and an apparatus, which relate to the technical field of intelligent driving. A specific implementation solution is: determining a first positioning result using point cloud data collected by lidar in combination with a laser point cloud reflection value map; constructing a constraint condition using the first positioning result, where the constraint condition is used to accelerate a convergence speed of solving a receiver position using observation data; performing GNSS-PPP positioning using the constraint condition in combination with observation data of a GNSS receiver to obtain a second positioning result. Using this solution, lidar positioning technology is combined with GNSS-PPP positioning technology to realize a purpose of not relying on a GNSS base station.

Positioning method and apparatus

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.

Method, apparatus, electronic device and readable storage medium for point cloud data processing

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 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.

Data processing method, data processing apparatus, electronic device and storage medium

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.

The embodiments provide a method for unmanned vehicle cruising, an unmanned vehicle and a storage medium, the method includes: in a state that a slow cruising function is started, cruising according to a preset cruising mode, and collecting running data through a sensing device, where the running data is data of an environment in which a vehicle locates, collected by the vehicle during a running process; and generating a map based on the collected running data. The embodiments of the present disclosure solve the problem that an unmanned vehicle in the prior art cannot update a map in time and, in particular, cannot develop a more suitable map according to different surrounding environments.

Method for unmanned vehicle cruising, unmanned vehicle and storage medium

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.

Positioning method, electronic device and computer readable storage medium

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/method generates a driving trajectory for an autonomous driving vehicle (ADV). The system perceives an environment of an autonomous driving vehicle (ADV). The system determines one or more bounding conditions based on the perceived environment. The system generates a first trajectory using a neural network model, wherein the neural network model is trained to generate a driving trajectory. The system evaluates/determines if the first trajectory satisfies the one or more bounding conditions. If the first trajectory satisfies the one or more bounding conditions, the system controls the ADV autonomously according to the first trajectory. Otherwise, the system controls the ADV autonomously according to a second trajectory, where the second trajectory is generated based on an objective function, where the objective function is determined based on at least the one or more bounding conditions.

Hybrid planning system for autonomous vehicles

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.

Method for generating a high precision map, apparatus and storage medium

The present application provides a method for detecting map error information, an apparatus, a device, a vehicle and a storage medium, where the method includes: acquiring current environmental feature information around a vehicle; and detecting, according to the current environmental feature information and map data, whether the map data is erroneous. Whether the map data is erroneous is detected in real time based on the current environmental feature information during the process of the actual traveling of the vehicle, which improves timeliness and efficiency for the map error detection, thereby the map data can be corrected in time and the traveling safety of the vehicle may be improved.

Method for detecting map error information, apparatus, device, vehicle and storage medium

The present application provides a self-driving vehicle positioning method, an apparatus and a storage medium, where the method includes: obtaining reference data, where the reference data includes: state information and running path information of at least one traffic participant within a preset distance from a self-driving vehicle, and updating positioning information of the self-driving vehicle according to the reference data. In the technical solutions, when a GPS signal is weak, the self-driving vehicle can also update the positioning information in time, thereby ensuring driving safety of the self-driving vehicle, and solving a driving safety problem existing in the self-driving vehicle of the prior art.

Self-driving vehicle positioning method, apparatus and storage medium

In one embodiment, a system is disclosed for registration of point clouds for autonomous driving vehicles (ADV). The system receives a number of point clouds and corresponding poses from ADVs equipped with LIDAR sensors capturing point clouds of a navigable area to be mapped, where the point clouds correspond to a first coordinate system. The system partitions the point clouds and the corresponding poses into one or more loop partitions based on navigable loop information captured by the point clouds. For each of the loop partitions, the system applies an optimization model to point clouds corresponding to the loop partition to register the point clouds. They system merges the one or more loop partitions together using a pose graph algorithm, where the merged partitions of point clouds are utilized to perceive a driving environment surrounding the ADV.

Point clouds registration system for autonomous vehicles

Growing community of inventors

Beijing, China

Shiyu Song