We present an innovative approach that integrates radar data with high-definition (HD) maps to generate a robust perception module suitable for cost-map generation. While radars are essential for perception through instant velocity measurement, high range, and their robustness to weather conditions, they often introduce errors such as false alarms and clutters that could mislead the cost-map generation. Through a detailed analysis of these errors, including their typical spatial patterns on the HD map, we developed a precise classifier capable of removing these misdetections from actual objects. Experimental validation of our methodology is conducted on a dataset collected using our autonomous shuttle-bus, WATonoBus, under various weather conditions. Find a sample of our work's performance by visiting MVS-lab channel, where the perception output is put into a cost-map.


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    Title :

    Robust Radar Object Detection Using HD Map Likelihoods and Information


    Contributors:


    Publication date :

    2024-09-24


    Size :

    1147990 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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