CASPER: A Design Study for a Clutter-Aware Semantic Perception Framework for LiDAR Odometry in Dynamic Industrial Warehouse Environments

Note on scope: CASPER is a proposed architecture. The full integration of LIO-SAM and RandLA-Net described in Section 3 was not implemented. Experiments were conducted in PyBullet using ICP odometry and oracle semantic labels (perfect classification). Results validate the class-conditioned dynamic-object exclusion principle in simulation only; they do not represent the performance of the complete CASPER system. Simulation experiments employ ICP odometry with oracle semantic labels to validate the class-conditioned dynamic exclusion principle in isolation; the full LIO-SAM backbone integration, IMU preintegration, and semantic loop closure verification constitute design specifications for which experimental validation is pending. This paper provides preliminary architectural and simulation evidence supporting the proposed class-conditioned semantic exclusion principle, together with a taxonomy of warehouse-specific SLAM failure modes, a semantic class hierarchy, and reproducible design principles for LiDAR-based semantic robot navigation. Full hardware implementation and real-world benchmarking remain future priorities.