RadarSLAM: Radar based Large-Scale SLAM in All Weathers

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.

I. Introduction

Simultaneous Localization and Mapping (SLAM) has been extensively investigated with numerous sensor modalities, e.g., sonar, camera and LiDAR, in the last decades. However, for outdoor large-scale SLAM, ensuring its robust operation is still very challenging especially in adverse weather conditions. Recently, the emerging Frequency-Modulated Continuous Wave (FMCW) radar sensors which can work in various weathers have been increasingly adopted for self-diving cars and autonomous robots. Therefore, an interesting yet open question is whether these radars can be used for robust SLAM in large-scale environments in extreme weather conditions, such as heavy snowfall.