Relocalization based on millimeter wave radar point cloud for visually degraded environments

Simultaneously localization and mapping (SLAM) has been widely used in autonomous mobile systems to fulfill autonomous navigation. Relocalization plays an important role in SLAM for closing the loop and eliminating the drift of pose estimation. Traditional methods mostly rely on LiDAR or camera sensors, which may degrade or even fail in rainy or dusty situations or with large illumination changes. In this article, we explore the use of low-cost commercial millimeter wave (mmWave) radars and propose a noval mmWave radar point cloud-based relocalization method. Our method first pre-processes the radar point cloud and, based on that, achieves fast 3-DOF pose estimation for the robot. We build a prototype and thoroughly evaluate our method using data sets collected by our platform in four complex environments, including street, park, road, and water surface scenarios. The experimental results show that our method consistently outperforms other baseline methods including the vision-based counterparts, especially in the visual degraded scenes.