vMF Distribution-Based Particle Filtering for Range-Only SLAM

Range-Only Simultaneous Localization and Mapping (RO-SLAM) is crucial for mobile robot navigation. However, the lack of directional information in distance measurements causes significant nonlinearity and convergence issues. These challenges are particularly prominent during beacon initialization. During this phase, joint estimation of the robot pose and beacon positions becomes difficult. To tackle this problem, we propose a novel initialization algorithm using particle filtering. This algorithm models beacon position uncertainty using von Mises–Fisher (vMF) distributions. It also introduces an innovative resampling strategy with dynamically adjusted particle numbers. Building on this initialization, we construct a comprehensive 3D RO-SLAM framework. Both simulation and real-world experiments show that the proposed algorithm provides accurate and efficient estimation of robot pose and beacon positions. These results demonstrate improvements in both initialization accuracy and speed.