Clean and consistent MLS point clouds from the start: Moving-object removal coupled with LIDAR-inertial odometry for urban scenes

Mapping in urban environments is often disrupted by moving objects, which contaminate LiDAR observations. To address this issue, we propose a complete LiDAR-inertial odometry framework designed to ensure map consistency and cleanness for mobile laser mapping systems. Our approach features three key innovations: (1) We design a visibility-based moving-object removal module that utilizes motion cues and object-level clustering to consistently detect and eliminate points from moving entities. (2) We propose a novel two-pass strategy that couples moving-object removal with LiDAR-inertial odometry. The first pass provides priors for moving-object filtering, while the second pass refines the trajectory using only points from static objects. Crucially, this iterative update builds a clean map from the start while maintaining real-time performance. (3) In addition to real-time mapping, the framework is scalable for professional large-scale mobile mapping. We add a global pose graph optimization module with long-range revisit and short-range co-visibility constraints to further optimize trajectories and improve map consistency. Experiments on large-scale public datasets and challenging private sequences demonstrate that the framework achieves state-of-the-art performance in moving-object removal (achieving the best F1 score in 7 out of 8 sequences) and outperforms state-of-the-art general-purpose and dynamic-aware LIO methods (achieving the lowest trajectory error in all 8 sequences). Furthermore, our experiments confirm that superior trajectories systematically enhance moving-object removal, validating the synergistic effect between map cleanness and consistency. The code will be available at https://github.com/Shidabot/CC-MLS.