MS-SLAM: Multiple Input Multiple Output Synthetic Aperture Radar Simultaneous Localization and Mapping

In this paper we propose a radar-only simultaneous localization and mapping algorithm based on multiple input multiple output synthetic aperture radar images. The algorithm distinguishes itself from others by depending only on radar data for generating synthetic aperture radar images for estimating traversed trajectory and building a visual representation. In our algorithm, ego-velocity (estimated using only radar data) is used for generating synthetic aperture radar images. The generated radar images are used for rotation estimation in the odometry step as well as for place recognition by exploiting the Fourier-Radon image registration approach. After the trajectory is optimized, we combine coherent and incoherent processing over the radar data for generating a map of the traversed area. The proposed concept was evaluated over multiple sequences comprising heterogeneous and dynamic environments. The results show high performance of the algorithm in terms of place recognition, attaining a balanced f-score in the range of 0.86–0.96. Moreover, the algorithm also achieves good results in terms of simultaneous localization and mapping. For example, it achieves an absolute trajectory error of 0.11 m for a trajectory of length 340 m, and 0.43 m for a trajectory of length 1092 m. Finally, we also include a case study in which we show the capability of the radar-only localization and mapping solution in operating under scenarios that are challenging for global navigation satellite systems.