TY - GEN
T1 - A Normal Distribution Transform-Based Radar Odometry Designed For Scanning and Automotive Radars
AU - Kung, Pou Chun
AU - Wang, Chieh-Chih
AU - Lin, Wen-Chieh
N1 - Publisher Copyright:
© 2021 IEEE
PY - 2021/6
Y1 - 2021/6
N2 - 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.
AB - 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.
KW - Autonomous Driving
KW - Odometry
KW - Radar
KW - Scan Matching
UR - http://www.scopus.com/inward/record.url?scp=85124496838&partnerID=8YFLogxK
U2 - 10.1109/ICRA48506.2021.9561413
DO - 10.1109/ICRA48506.2021.9561413
M3 - Conference contribution
AN - SCOPUS:85124496838
T3 - Proceedings - IEEE International Conference on Robotics and Automation
SP - 14417
EP - 14423
BT - 2021 IEEE International Conference on Robotics and Automation, ICRA 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2021 IEEE International Conference on Robotics and Automation, ICRA 2021
Y2 - 30 May 2021 through 5 June 2021
ER -