Pony Ai Inc.

Mengda Yang

Weixin Jiang

Chuanchuan Liu

Ruimeng Shi

Yuyang Ding

Xiang Yu

Tiancheng Lou

Jun Peng

Ke Li

Hao Song

Sinan Xiao

Michael C Wu

Tianyi Li

Piaoyang Cui

Haoying Fu

Zheng Yan

An apparatus includes a processing node of a distributed computing platform. The processing node communicates with other processing nodes over one or more networks. The processing node may receive frames of point clouds at a processing node of a distributed computing platform, determine a subset of the frames as key frames based at least in part on distances travelled between captures of the respective frames, and allocate tasks of processing the key frames to processing subnodes based at least in part on estimated processing demands of the key frames and processing capabilities of each of the processing subnodes.

Distributed computing network to perform simultaneous localization and mapping

A computing system includes one or more processors and a memory storing instructions that, when executed by the one or more processors, cause the system to perform operations. The operations include obtaining sensor data from a sensor of a vehicle, the sensor data including point cloud frames at different positions, orientations, and times, the sensor data used to generate a map, determining a position and an orientation of the sensor corresponding to a capture of each of the point cloud frames according to a simultaneous localization and mapping (SLAM) algorithm, and depicting, on an interface, a graphical illustration of the determined positions at which the point cloud frames were captured.

Contextualization and refinement of simultaneous localization and mapping

A vehicle generates a city-scale map. The vehicle includes one or more Lidar sensors configured to obtain point clouds at different positions, orientations, and times, one or more processors, and a memory storing instructions that, when executed by the one or more processors, cause the system to perform registering, in pairs, a subset of the point clouds based on respective surface normals of each of the point clouds; determining loop closures based on the registered subset of point clouds; determining a position and an orientation of each of the subset of the point clouds based on constraints associated with the determined loop closures; and generating a map based on the determined position and the orientation of each of the subset of the point clouds.

High-definition city mapping

A computing system includes one or more processors and a memory storing instructions that, when executed by the one or more processors, cause the system to perform operations. The operations include determining that a portion of an existing map is to be updated; obtaining a point cloud acquired by one or more Lidar sensors corresponding to a location of the portion; converting the portion into an equivalent point cloud; performing a point cloud registration based on the equivalent point cloud and the point cloud; and updating the existing map based on the point cloud registration.

Correcting or expanding an existing high-definition map

Systems, methods, and non-transitory computer readable media are provided for obtaining a slice of static map data comprising a plurality of blocks, each block comprising a plurality of cells, each, each cell having a cell value indicating a probability that an object is present in the cell; loading the slice into a cache memory of a parallel processor; arranging the static map data in the cache memory in contiguous memory spaces assigned to a group of workers of the parallel processor that have coalescing constraints; loading a frame of dynamic map data into the cache memory; obtaining a plurality of scan match candidates each representing a possible position and attitude of the vehicle; processing, in the parallel processor, the static and dynamic map data and the candidates to generate results each representing a candidate and score; and selecting the candidate having the highest score as a vehicle position.

Autonomous vehicle navigation using with coalescing constraints for static map data

Systems and methods are provided for segmenting a data frame to be acquired into a number of incremental data of equal data length. A first incremental data of the data frame can be acquired from one or more sensors. The first incremental data of the data frame can be processed while a next incremental data of the data frame is being acquired from the one or more sensors. The acquiring and processing of incremental data of the data frame can continue until a last incremental data of the data frame is acquired and processed. Processed incremental data can be outputted as a processed data frame.

Growing community of inventors

Sunnyvale, CA, United States of America

Mengda Yang