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

Baidu USA LLC

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

International Business Machines Corporation

Beijing Baidu Netcom Science and Technology Co., Ltd.

Beijing Baidu Netcom Science Technology Co., Ltd

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

The Procter Gamble Company

Datang Mobile Communications Equipment Co., Ltd.

Beijing Sankuai Online Technology Co., Ltd

Beijing Migrasome Therapeutics Limited

Li Yu

Shiyu Song

Pengfei Yuan

Runxin He

Fangfang Dong

Yong Xiao

Shichun Yi

Cheng Fang Wang

Wendong Ding

Baoqiang Xu

Guowei Wan

Liang Peng

Weixin Lu

Xin Jin

Koichi Morimoto

Shuo Wang

Guo Tong Xie

Ling Tong

Chunmin Cheng

Jing Mei

Haifeng Liu

Tianlei Zhang

Sheng Liu

Jin Yu

Fengli Wang

Fan Yi

Shengpan Xu

Yi Lv

Dongju Wang

Zhiguang Hu

Chen Dai

Qiushi Zhong

Yi Zheng

Moye Jia

Tianlun Ding

Weilin Peng

Hailong Tian

SuYing Rui

Masa Nishikawa

The present invention relates to the field of biomedicine, and in particular to an engineered migrasome, a method for preparing the engineered migrasome, a delivery system comprising the engineered migrasome, and a method for preparing the delivery system.

Engineered migrasome, method for preparing same, and uses thereof

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 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

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

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

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 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

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

Methods and apparatuses for generating a dynamic picture sequence are provided. The method includes generating a target vector from a target static picture; obtaining a foreground dynamic picture sequence, a foreground mask dynamic picture sequence and a background static picture by performing a prediction on the target vector; obtaining N frames of foreground static pictures by splitting the foreground dynamic picture sequence; obtaining N frames of foreground mask static pictures by splitting the foreground mask dynamic picture sequence; generating a first frame of static image by performing a prediction with inputting a first frame of the foreground static pictures, a first frame of the foreground mask static pictures and the background static picture into a first cell body of a Long Short-Term Memory network.

Generation of dynamic picture sequence

Embodiments of the present disclosure provide a method and a device for acquiring a map, a device, and a computer readable storage medium. The method includes: acquiring a reference map of an acquisition area; dividing the acquisition area into a plurality of sub-areas based on the reference map; generating a plurality of acquisition tasks corresponding to the plurality of sub-areas, wherein each of the plurality of acquisition tasks is configured to acquire a map of each of the plurality of sub-areas; and assigning each of the plurality of acquisition tasks to an acquisition entity, to enable the acquisition entity to acquire the map of each of the plurality of sub-areas.

Method, device and apparatus for acquiring map, and computer readable storage medium

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.

RGB point clouds based map generation system for autonomous vehicles

Embodiments of the present disclosure relate to a method, apparatus, device and computer readable storage medium for reconstructing a three-dimensional scene. The method for reconstructing a three-dimensional scene includes acquiring a point cloud data frame set for a three-dimensional scene, point cloud data frames in the point cloud data frame set respectively having a pose parameter. The method further comprises determining a subset corresponding to a part of the three-dimensional scene from the point cloud data frame set. The method further comprises adjusting a pose parameter of a point cloud data frame in the subset to obtain an adjusted subset, the adjusted subset including at least two point cloud data frames having matching overlapping parts. The method further comprises updating the point cloud data frame set using the adjusted subset. In this way, distributed processing on a large amount of point cloud data may be realized.

Method, apparatus, device and computer readable storage medium for reconstructing three-dimensional scene

Growing community of inventors

Beijing, China

Li Yu