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.
MACHINE LEARNING BASED LANDMARK PERCEPTION FOR LOCALIZATION IN AUTONOMOUS SYSTEMS AND APPLICATIONS
2024-08-22
Patent
Electronic Resource
English
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 |
Monocular Visual Localization for Autonomous Vehicles Based on Lightweight Landmark Map
SAE Technical Papers | 2022
|Monocular Visual Localization for Autonomous Vehicles Based on Lightweight Landmark Map
British Library Conference Proceedings | 2022
|Landmark Based Autonomous Snowplow Navigation
British Library Conference Proceedings | 2016
|