Radio Slam with Hybrid Sensing for Mixed Reflection Type Environments

Abstract

Radio simultaneous localization and mapping (SLAM) with active sensing, such as radar and LiDAR, faces difficulty in detecting mirror-like walls that cause specular reflection. To solve this problem, the proposed radio SLAM algorithm merges active and passive sensing. Passive sensing exploits low-frequency radio signals that are specularly reflected from objects. However, maps created by active and passive sensing have different characteristics. Thus, the proposed algorithm fuses heterogeneous maps using Dirichlet process-based clustering to create one integrated map and improve mapping accuracy. Simulation results demonstrate that the proposed radio SLAM algorithm outperforms the classical methods only with active or passive sensing in mixed reflection type environments.