LiDAR SLAM improves tunnel navigation with new geometric landmarks
Degeneracy-Orthogonal Geometric Constraints for LiDAR SLAM
Robotics
Summary
Robots navigating long tunnels often lose track of their position because the environment looks very similar along their path. The authors created a new way to identify unique shapes on tunnel walls, like pipe joints, to help robots know exactly where they are. This method uses tiny differences in surface shapes to find and match these landmarks, reducing errors in robot movement estimates. Testing showed it works well to keep robot positions accurate even in difficult tunnel environments.
What this means in practice
- •For industrial robotics teams: Enable robots to maintain precise location in tunnels and pipelines by correcting position drift using new geometric landmark matching.
- •For mapping and surveying firms: Produce accurate maps of long uniform corridors by stabilizing drone or robot trajectories via enhanced landmark detection.
Authors
Minseo Kim, Yina Kim, Jinhwa Hwang, Alex Junho Lee
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.