Geometry-aware Subsampling and pole-enhanced Map Constraints for urban Localization of LiDAR-based Systems

Urban localization for autonomous driving requires accurate 6-DoF vehicle pose despite GNSS multipath, occlusions, and rapidly changing visibility. We fuse LiDAR, IMU, and GNSS in an error-state Kalman filter against a high-resolution (HR) map, aiming (i) to reduce LiDAR load without degrading accuracy and (ii) to improve robustness in building-sparse areas such as open junctions. The reference trajectory and HR map stem from a dedicated urban measurement campaign; Monte-Carlo simulations use ray-cast LiDAR, synthesized IMU, and GNSS tied to this trajectory so that only sensor noise is varied. A geometry-aware farthest-point sampling scheme prioritizes points informative for building/ground planes and pole-like structures, while an extended functional model introduces poles as additional vertical constraints. A retained-point rate of 10 % preserves trajectory-wide millimeter-level and sub-milliradian accuracy, meeting in theory automotive requirements. Filter runtime is reduced by about 82 % relative to the full LiDAR data. Compared with plane-only variants, the planes+poles configuration yields statistically significant but globally modest improvements in longitudinal, lateral, and yaw accuracy. More importantly, a sliding-window analysis reveals that it markedly stabilizes pose in plane-sparse junctions. Overall, the results suggest that task-aware subsampling preserves trajectory-wide performance while pole constraints add local robustness in challenging urban scenes; validation with real sensor logs remains necessary to confirm these accuracy margins, but the proposed filtering scheme shows promising potential for practical deployment.