AutoPlace: Robust Place Recognition with Single-chip Automotive Radar

Kaiwen Cai, Bing Wang, Chris Xiaoxuan Lu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

This paper presents a novel place recognition approach to autonomous vehicles by using low-cost, single-chip automotive radar. Aimed at improving recognition robustness and fully exploiting the rich information provided by this emerging automotive radar, our approach follows a principled pipeline that comprises (1) dynamic points removal from instant Doppler measurement, (2) spatial-temporal feature embedding on radar point clouds, and (3) retrieved candidates refinement from Radar Cross Section measurement. Extensive experimental results on the public nuScenes dataset demonstrate that existing visual/LiDAR/spinning radar place recognition approaches are less suitable for single-chip automotive radar. In contrast, our purpose-built approach for automotive radar consistently outperforms a variety of baseline methods via a comprehensive set of metrics, providing insights into the efficacy when used in a realistic system.
Original languageEnglish
Title of host publicationProceedings of 2022 IEEE International Conference on Robotics and Automation
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages2222-2228
Number of pages7
ISBN (Electronic)978-1-7281-9681-7
ISBN (Print)978-1-7281-9682-4
DOIs
Publication statusPublished - 12 Jul 2022
Event2022 IEEE International Conference on Robotics and Automation - Philadelphia , United States
Duration: 23 May 202227 May 2022
https://www.icra2022.org/

Conference

Conference2022 IEEE International Conference on Robotics and Automation
Abbreviated titleICRA 2022
Country/TerritoryUnited States
CityPhiladelphia
Period23/05/2227/05/22
Internet address

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