Virtual-AMR LiDAR Fusion for AMCL Localization Under Mutual Occlusion

When autonomous mobile robots (AMRs) travel in formation, mutual occlusion reduces the map features available to adaptive Monte Carlo localization (AMCL) and introduces robot body returns. This study proposes a Virtual-AMR LiDAR fusion method that transforms synchronized 2D scans into a common frame, tracks robot contours, estimates relative pose by KD-tree overlap matching, removes robot returns, and fuses environmental measurements for AMCL. The method was evaluated using two AMRs in a 3.1 m × 10 m indoor area with 4 m straight-line and turning trajectories. Four branch-wise configurations were tested in five repeated trials. Against an internal reference trajectory constructed from the planned path and motor encoder measurements rather than an independent ground truth system, the complete method achieved trial-level 2D RMSE values of 0.0412 ± 0.0083 m for the straight-line trajectory and 0.0511 ± 0.0104 m for the turning trajectory (mean ± sample standard deviation across five trials), while reducing localization drift and preventing the AMCL divergence observed under the tested mutual occlusion conditions.