Paper Info
Title
RadarSLAM: A robust simultaneous localization and mapping system for all weather conditions
Abstract
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.
Year
DOI
Venue
2022
10.1177/02783649221080483
INTERNATIONAL JOURNAL OF ROBOTICS RESEARCH
Keywords
DocType
Volume
radar sensing, simultaneous localization and mapping, all-weather perception
Journal
41
Issue
ISSN
Citations 
5
0278-3649
0
PageRank 
References 
Authors
0.34
0
4
Name
Order
Citations
PageRank
Hong Ziyang100.68
Yvan Petillot200.34
Andrew Wallace300.34
Sen Wang427921.15