A Normal Distribution Transform-Based Radar Odometry Designed For Scanning and Automotive Radars

研究成果: Conference contribution同行評審

29 引文 斯高帕斯(Scopus)

摘要

Existing radar sensors can be classified into automotive and scanning radars. While most radar odometry (RO) methods are only designed for a specific type of radar, our RO method adapts to both scanning and automotive radars. Our RO is simple yet effective, where the pipeline consists of thresholding, probabilistic submap building, and an Normal Distribution Transform-based (NDT-based) radar scan matching. The proposed RO has been tested on two public radar datasets: the Oxford Radar RobotCar dataset and the nuScenes dataset, which provide scanning and automotive radar data respectively. The results show that our approach surpasses state-of-the-art RO using either automotive or scanning radar by reducing translational error by 51% and 30%, respectively, and rotational error by 17% and 29%, respectively. Besides, we show that our RO achieves centimeter-level accuracy as lidar odometry, and automotive and scanning RO have similar accuracy.

原文English
主出版物標題2021 IEEE International Conference on Robotics and Automation, ICRA 2021
發行者Institute of Electrical and Electronics Engineers Inc.
頁面14417-14423
頁數7
ISBN(電子)9781728190778
DOIs
出版狀態Published - 6月 2021
事件2021 IEEE International Conference on Robotics and Automation, ICRA 2021 - Xi'an, China
持續時間: 30 5月 20215 6月 2021

出版系列

名字Proceedings - IEEE International Conference on Robotics and Automation
2021-May
ISSN(列印)1050-4729

Conference

Conference2021 IEEE International Conference on Robotics and Automation, ICRA 2021
國家/地區China
城市Xi'an
期間30/05/215/06/21

指紋

深入研究「A Normal Distribution Transform-Based Radar Odometry Designed For Scanning and Automotive Radars」主題。共同形成了獨特的指紋。

引用此