RadarSLAM: A robust simultaneous localization and mapping system for all weather conditions

被引:37
作者
Hong, Ziyang [1 ]
Petillot, Yvan [1 ]
Wallace, Andrew [1 ]
Wang, Sen [1 ]
机构
[1] Heriot Watt Univ, Edinburgh Ctr Robot, Edinburgh, Midlothian, Scotland
基金
欧盟地平线“2020”; 英国工程与自然科学研究理事会;
关键词
radar sensing; simultaneous localization and mapping; all-weather perception; SLAM; VERSATILE;
D O I
10.1177/02783649221080483
中图分类号
TP24 [机器人技术];
学科分类号
080202 ; 1405 ;
摘要
A Simultaneous Localization and Mapping (SLAM) system must be robust to support long-term mobile vehicle and robot applications. However, camera and LiDAR based SLAM systems can be fragile when facing challenging illumination or weather conditions which degrade the utility of imagery and point cloud data. Radar, whose operating electromagnetic spectrum is less affected by environmental changes, is promising although its distinct sensor model and noise characteristics bring open challenges when being exploited for SLAM. This paper studies the use of a Frequency Modulated Continuous Wave radar for SLAM in large-scale outdoor environments. We propose a full radar SLAM system, including a novel radar motion estimation algorithm that leverages radar geometry for reliable feature tracking. It also optimally compensates motion distortion and estimates pose by joint optimization. Its loop closure component is designed to be simple yet efficient for radar imagery by capturing and exploiting structural information of the surrounding environment. Extensive experiments on three public radar datasets, ranging from city streets and residential areas to countryside and highways, show competitive accuracy and reliability performance of the proposed radar SLAM system compared to the state-of-the-art LiDAR, vision and radar methods. The results show that our system is technically viable in achieving reliable SLAM in extreme weather conditions on the RADIATE Dataset, for example, heavy snow and dense fog, demonstrating the promising potential of using radar for all-weather localization and mapping.
引用
收藏
页码:519 / 542
页数:24
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