Degeneracy-Orthogonal Geometric Constraints for LiDAR SLAM

2026-09-29

AI Summary

This study addresses the longitudinal drift problem in LiDAR odometry caused by structural degeneracy in axisymmetric environments such as long tunnels. We propose DeCOD, a novel descriptor that characterizes cross-sectional profile offsets orthogonal to the degenerate direction. This approach decouples first-order errors and eliminates heading ambiguity, enabling longitudinal drift correction through geometric constraints within pose graph optimization while preserving rotational degrees of freedom. The technical framework integrates 3D point cloud processing, geometric feature extraction, and distortion estimation. Evaluations on public benchmarks and real-world field experiments demonstrate that the proposed method achieves robust landmark retrieval and effectively stabilizes trajectories generated by diverse front-end odometry systems.

Abstract

Autonomous robot navigation relies on simultaneous localization and mapping (SLAM) to estimate motion and maintain an accurate pose within an environment. However, in axially uniform corridors such as long tunnels and pipelines, LiDAR odometry is fundamentally limited by unconstrained drift along the feature-weak travel direction. This structural degeneracy cannot be resolved by local scan matching alone. To address this challenge, we propose the Degeneracy-orthogonal Contour Offset Descriptor (DeCOD), a structure-aligned geometric descriptor for cross-sectional landmarks. Cross-sectional boundaries, such as pipe joints and structural rings, provide metric constraints along this degenerate axis, but distinguishing individual landmarks requires capturing subtle surface variations across nearly identical profiles. The descriptor parameterizes signed normal deviation from estimated boundary contours, and matching explicitly resolves heading ambiguity and decouples first-order contour errors by distortion estimation. Matched landmarks yield geometric factors that enforce agreement in cross-section position and corridor axis alignment during pose-graph optimization, correcting longitudinal drift while leaving rotation about the common axis unconstrained. On a public benchmark and in field experiments, DeCOD achieves robust landmark retrieval over standard 3D descriptors and successfully stabilizes trajectories across different odometry frontends, reliably constraining longitudinal drift under geometric degeneracy.

Problem

• LiDAR SLAM
• geometric degeneracy
• longitudinal drift
• axially uniform corridors

Innovation

• Degeneracy-Orthogonal Descriptor
• LiDAR SLAM
• Geometric Constraints
• Pose-Graph Optimization
• Structural Degeneracy