MAP CREATION AND LOCALIZATION FOR AUTONOMOUS DRIVING APPLICATIONS

    公开(公告)号:US20210063199A1

    公开(公告)日:2021-03-04

    申请号:US17008100

    申请日:2020-08-31

    Abstract: An end-to-end system for data generation, map creation using the generated data, and localization to the created map is disclosed. Mapstreams—or streams of sensor data, perception outputs from deep neural networks (DNNs), and/or relative trajectory data—corresponding to any number of drives by any number of vehicles may be generated and uploaded to the cloud. The mapstreams may be used to generate map data—and ultimately a fused high definition (HD) map—that represents data generated over a plurality of drives. When localizing to the fused HD map, individual localization results may be generated based on comparisons of real-time data from a sensor modality to map data corresponding to the same sensor modality. This process may be repeated for any number of sensor modalities and the results may be fused together to determine a final fused localization result.

    DETERMINING LOCALIZATION ACCURACY IN AUTONOMOUS AND SEMI-AUTONOMOUS SYSTEMS AND APPLICATIONS

    公开(公告)号:US20250058796A1

    公开(公告)日:2025-02-20

    申请号:US18446649

    申请日:2023-08-09

    Abstract: In various examples, accuracy determinations for localization in autonomous and semi-autonomous systems and applications are described herein. Systems and methods are disclosed that determine one or more errors associated with vehicle localization using various types of sensor data generated using a vehicle. For instance, a first component of the vehicle may use a map and first sensor data to determine an estimated pose of the vehicle. A second component of the vehicle may then determine the error(s) associated with the estimated pose based on both actual motion of the vehicle within the environment, as determined using second sensor data, and comparing features represented by the first sensor data to features represented by the map. In some examples, the second component may further determine information associated with the error(s), such as one or more uncertainties associated with the error(s).

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