Systems and methods for localization are provided. In one aspect, a LIDAR scan is captured from above to generate a point cloud. One or more locations may be sampled in the point cloud and LIDAR scans may be simulated at each location. The sampled locations and associated simulated LIDAR scans may be used to train a regressor to localize vehicles in the environment that are at poses different from the pose from which the LIDAR point cloud was captured. In one aspect, a mapping UAV systematically scans an environment with a camera to generate a plurality of map images. The map images are stitched together into an orthographic image. A runtime UAV captures one or more runtime images of the environment with a camera. Feature matching is performed between the runtime images and the orthographic image for localization. In one aspect, a first machine learning model is trained to transform a camera image into a LIDAR image and a second machine learning model is trained to estimate a pose based on a LIDAR image. A runtime image may be input to the first machine learning model to generate a simulated LIDAR scan. The simulated LIDAR scan may be input to the second machine learning model to estimate a pose, which localizes the vehicle.


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    Title :

    SYSTEMS AND METHODS FOR LOCALIZATION


    Contributors:
    SIDDIQUI MATHEEN (author) / LIN YUPING (author) / LIN CHENG-YI (author) / YUAN CHANG (author)

    Publication date :

    2020-09-24


    Type of media :

    Patent


    Type of material :

    Electronic Resource


    Language :

    English


    Classification :

    IPC:    G01S RADIO DIRECTION-FINDING , Funkpeilung / B64C AEROPLANES , Flugzeuge / G06K Erkennen von Daten , RECOGNITION OF DATA / G06N COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS , Rechnersysteme, basierend auf spezifischen Rechenmodellen



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