Lidar-Level Localization With Radar? The CFEAR Approach to Accurate, Fast, and Robust Large-Scale Radar Odometry in Diverse Environments

被引:21
作者
Adolfsson, Daniel [1 ]
Magnusson, Martin [1 ]
Alhashimi, Anas [1 ,2 ,3 ]
Lilienthal, Achim J. [1 ,2 ,3 ]
Andreasson, Henrik [1 ]
机构
[1] Orebro Univ, AASS Res Ctr, Mobile Robot & Olfact Lab, Orebro 70182, Sweden
[2] Univ Baghdad, Comp Engn Dept, Baghdad 10071, Iraq
[3] Tech Univ Munich, Percept Intelligent Syst, D-80333 Munich, Germany
关键词
Radar; Sensors; Spinning; Azimuth; Simultaneous localization and mapping; Estimation; Location awareness; Localization; radar odometry; range sensing; SLAM; EGO-MOTION ESTIMATION; REGISTRATION;
D O I
10.1109/TRO.2022.3221302
中图分类号
TP24 [机器人技术];
学科分类号
080202 ; 1405 ;
摘要
This article presents an accurate, highly efficient, and learning-free method for large-scale odometry estimation using spinning radar, empirically found to generalize well across very diverse environments-outdoors, from urban to woodland, and indoors in warehouses and mines-without changing parameters. Our method integrates motion compensation within a sweep with one-to-many scan registration that minimizes distances between nearby oriented surface points and mitigates outliers with a robust loss function. Extending our previous approach conservative filtering for efficient and accurate radar odometry (CFEAR), we present an in-depth investigation on a wider range of datasets, quantifying the importance of filtering, resolution, registration cost and loss functions, keyframe history, and motion compensation. We present a new solving strategy and configuration that overcomes previous issues with sparsity and bias, and improves our state-of-the-art by 38%, thus, surprisingly, outperforming radar simultaneous localization and mapping (SLAM) and approaching lidar SLAM. The most accurate configuration achieves 1.09% error at 5 Hz on the Oxford benchmark, and the fastest achieves 1.79% error at 160 Hz.
引用
收藏
页码:1476 / 1495
页数:20
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