Baidu USA LLC

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

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

Beijing Baidu Netcom Science and Technology Co., Ltd.

Beijing Baidu Netcom Science Technology Co., Ltd

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

Weixin Lu

Shiyu Song

Guowei Wan

Yao Zhou

Shenhua Hou

Li Yu

Liang Peng

Liang Wang

Fangfang Dong

He Yan

Feilong Yan

Baoqiang Xu

Renlan Cai

Xiaolong Yang

Bosheng Wang

Shengpan Xu

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

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.

Deep learning-based feature extraction for LiDAR localization of autonomous driving vehicles

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.

LiDAR localization using 3D CNN network for solution inference in autonomous driving vehicles

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. A specific implementation solution is as follows: obtaining a first feature vector of each point in first point cloud data, and determining at least one first key point in the points in the first point cloud data according to the first feature vectors of respective points; according to the and a preset first conversion parameter between second point cloud data and the first point cloud data, obtaining second key points of the second point cloud data corresponding to the first key points in the at least one first key point, as candidate matching points; according to the first point cloud data, the second point cloud data and a preset search radius, determining at least one first neighboring point of the and at least one second neighboring point of the candidate matching point corresponding to the at least one first key point; determining a matching point with which the at least one first key point is registered, according to the at least one first neighboring point of the at least one first key point and the at least one second neighboring point of the candidate matching points.

Point cloud data processing method, apparatus, electronic device and computer readable storage medium

In one embodiment, a method for temporal smoothness in localization results for an autonomous driving vehicle includes: creating a probability offset volume that represents an overall matching cost between a first set of keypoints from the online point cloud and a second set of keypoints from a pre-built point cloud map for each of a series of sequential light detection and ranging (LiDAR) frames in an online point cloud. The method also includes compressing the probability offset volume into multiple probability vectors across a X dimension, a Y dimension and a yaw dimension; providing each probability vector of the probability offset volume to a number of recurrent neural networks (RNNs); and generating, by the RNNs, a trajectory of location results across the plurality of sequential LiDAR frames.

Lidar localization using RNN and LSTM for temporal smoothness in autonomous driving vehicles

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.

Stationary object detecting method, apparatus and electronic device

A method and apparatus for identifying laser point cloud data of an autonomous vehicle provided with a lidar. A specific implementation of the method includes: acquiring current pose information of the autonomous vehicle in a world coordinate system in response to receiving a latest frame of laser point cloud data collected by the lidar; acquiring from the cache, based on the current pose information, N&#xd7;N map blocks centered on map blocks corresponding to the current pose information in a preset three-dimensional grid map and pre-loaded into a cache and are; and executing, for each laser point data in the received laser point cloud data, the laser point data identification operations. The implementation realizes identifying whether each laser point data is a static laser point, and can improve the accuracy rate of identifying a laser point data obstacle.

Method and apparatus for identifying laser point cloud data of autonomous vehicle

The present disclosure discloses a method and apparatus for positioning a vehicle. In some embodiments, the method comprises: acquiring an a priori position of a to-be-positioned vehicle at a current positioning moment determined by performing a strapdown calculation between a previous positioning moment and the current positioning moment; determining a map area for searching in a laser point cloud reflected value map; matching a reflected value characteristic of a projection area generated by projecting a real-time laser point cloud, to obtain a map matching position according to a matching result; positioning, using the a priori position in combination with observation data of a vehicle-mounted global navigation satellite system (GNSS) receiver of the vehicle, to obtain a satellite positioning position; and fusing the a priori position, the map matching position and the satellite positioning position to generate a positioning result of positioning the vehicle at the current moment.

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Beijing, China

Weixin Lu