Numerous Simultaneous Localization and Mapping (SLAM) algorithms have been presented in last decade using different sensor modalities. However, robust SLAM in extreme weather conditions is still an open research problem. In this paper, RadarSLAM, a full radar based graph SLAM system, is proposed for reliable localization and mapping in large-scale environments. It is composed of pose tracking, local mapping, loop closure detection and pose graph optimization, enhanced by novel feature matching and probabilistic point cloud generation on radar images. Extensive experiments are conducted on a public radar dataset and several self-collected radar sequences, demonstrating the state-of-the-art reliability and localization accuracy in various adverse weather conditions, such as dark night, dense fog and heavy snowfall.
For the dataset, please check and download our RADIATE dataset (RAdar Dataset In Adverse weaThEr).
Ziyang Hong, Yvan Petillot and Sen Wang. RadarSLAM: Radar based Large-Scale SLAM in All Weathers. 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). [arXiv]