In various examples, one or more DNNs may be used to detect landmarks (e.g., lane lines) and regress a representation of their shape. A DNN may be used to jointly generate classifications of measured 3D points using one output head (e.g., a classification head) and regress a representation of one or more fitted shapes (e.g., polylines, circles) using a second output head (e.g., a regression head). In some embodiments, multiple DNNs (e.g., a chain of multiple DNNs or multiple stages of a DNN) are used to sequentially generate classifications of measured 3D points and a regressed representation of the shape of one or more detected landmarks. As such, classified landmarks and corresponding fitted shapes may be decoded and used for localization, navigation, and/or other uses.


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    Title :

    MACHINE LEARNING BASED LANDMARK PERCEPTION FOR LOCALIZATION IN AUTONOMOUS SYSTEMS AND APPLICATIONS


    Contributors:

    Publication date :

    2024-08-22


    Type of media :

    Patent


    Type of material :

    Electronic Resource


    Language :

    English


    Classification :

    IPC:    G01C Messen von Entfernungen, Höhen, Neigungen oder Richtungen , MEASURING DISTANCES, LEVELS OR BEARINGS / B60W CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION , Gemeinsame Steuerung oder Regelung von Fahrzeug-Unteraggregaten verschiedenen Typs oder verschiedener Funktion / G06V



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