Baidu USA LLC

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

Yong Xiao

Shiyu Song

Li Yu

Runxin He

Pengfei Yuan

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

In one embodiment, a system identifies a road to be navigated by an ADV, the road being captured by one or more point clouds from one or more LIDAR sensors. The system extracts road marking information of the identified road from the point clouds, the road marking information describing one or more road markings of the identified road. The system partitions the road into one or more road partitions based on the road markings. The system generates a point cloud map based on the road partitions, where the point cloud map is utilized to perceive a driving environment surrounding the ADV.

Map partition system for autonomous vehicles

In one embodiment, a system receives a stream of frames of point clouds from one or more LIDAR sensors of an ADV and corresponding poses in real-time. The system extracts segment information for each frame of the stream based on geometric or spatial attributes of points in the frame, where the segment information includes one or more segments of at least a first frame corresponding to a first pose. The system registers the stream of frames based on the segment information. The system generates a first point cloud map for the stream of frames based on the frame registration.

Real-time map generation system for autonomous vehicles

In one embodiment, a system generates an occupancy grid map based on an initial frame of point clouds. The system receives one or more subsequent frames of the point clouds. For each of the subsequent frames, the system updates an occupancy grid map based on the subsequent frame. The system identifies one or more problematic voxels based on the update, the system determines whether the problematic voxels belong to a wall object, and in response to determining that the problematic voxels belong to a wall object, the system flags the problematic voxels as ghost effect voxels for the subsequent frame.

Point clouds ghosting effects detection system for autonomous driving vehicles

In one embodiment, a system receives a number of point clouds captured by one or more LIDAR sensors of an ADV and corresponding poses. The system receives a number of RGB images from one or more image capturing sensors of the ADV. The system synchronizes the RGB images with the point clouds to obtain RGB point clouds. The system extracts features from the RGB point clouds, the features including contextual and spatial information of the RGB point clouds. The system registers the RGB point clouds based on the extracted features and generates a point cloud map based on the registration of the RGB point clouds.

Growing community of inventors

Sunnyvale, CA, United States of America

Yong Xiao