Robust and Efficient Bayesian Approach for Snapshot Radio Slam

Abstract

Radio-based simultaneous localization and mapping (SLAM) has the potential to provide precise localization and environmental sensing capabilities using millimeter wave (mmWave) signals. In this paper, we propose methods that address the robustness and computational complexity issues of existing bistatic snapshot radio SLAM algorithms. We introduce multi-hypothesis Bayesian approaches to enhance the robustness and accuracy of solving the SLAM problem. In addition, we introduce effective methods to reduce computational complexity using prior information. The developed methods are evaluated using experimental mmWave data using 5G waveforms and benchmarked with respect to state-of-the-art methods. The results imply that the proposed methods improve the accuracy, robustness, and efficiency of radio SLAM.