A Real-World Benchmark of Monte Carlo-Assisted EKF Odometry for Online Pose Estimation in 2D LiDAR SLAM.

This study evaluates an Adaptive Monte Carlo Localization-Extended Kalman Filter (AMCL-EKF) pose-estimation stack for repeatable 2D LiDAR SLAM in GPS-denied indoor inspection scenarios. AMCL was used as an online map-referenced correction source fused with LiDAR odometry and Inertial Measurement Unit (IMU) data, and the resulting pose estimate was supplied online to three SLAM backends: Cartographer, GMapping, and SLAM Toolbox. Experiments were performed with a wheeled Husarion Panther and a quadruped Boston Dynamics Spot in three indoor environments of different geometric complexity, producing 720 SLAM executions. Trajectory repeatability was assessed using SE(2)-aligned pairwise and centroid-based ATE-style dispersion and translational RPE, while map repeatability was evaluated with occupied-cell IoU. Accordingly, the metrics were used to quantify between-run dispersion rather than absolute accuracy against external ground-truth data. The results show that AMCL-EKF fusion is highly dependent on the environment, platform, and SLAM backend. AMCL improved selected configurations, especially for Spot in structured environments and for Panther map consistency, but degraded others in geometrically repetitive corridors and mixed-structure spaces. The study also shows that the presence of AMCL-assisted odometry correction alone does not determine final trajectory repeatability, because each SLAM backend incorporates the supplied fused pose estimate differently. The findings support confidence-aware AMCL integration and motivate integrated SLAM architectures resistant to over-correction. These results provide guidance for robust autonomous mapping and inspection with heterogeneous mobile robotic platforms in real environments.