Enhancing Graph-Based SLAM in GNSS-Denied environments by leveraging leg odometry

2026-05-19

AI Summary

This work addresses the failure of LiDAR-inertial SLAM in GNSS-denied environments, where geometrically sparse or repetitive terrain induces severe elevation drift. Building upon the LIO-SAM framework, we propose a novel factor graph architecture that integrates leg odometry—derived from proprioceptive gait control—as a lightweight vertical anchor within the graph optimization. This integration is achieved through a relative pose equality constraint with a selective noise model, tightly coupled with the primary LiDAR-inertial pipeline. Requiring no additional sensors, our method reduces elevation drift from over 30 meters to less than 30 centimeters in outdoor experiments exceeding one kilometer, and achieves stable convergence even in scenarios where baseline approaches completely fail.

Abstract

Autonomous navigation in GNSS-denied environments remains a core challenge for legged robots, where exteroceptive sensors such as LiDAR are prone to elevation drift in geometrically sparse or repetitive scenes. We present a factor graph architecture that augments the LIO-SAM framework with a parallel kinematic lane driven by proprioceptive leg odometry, coupled to the main LiDAR-inertial lane via an identity relative pose constraint with a selective noise model. Applied to a Linxai D50 quadruped platform across two outdoor loops totaling over one kilometer, our approach reduces elevation drift from over 30m to under 30cm and enables convergence in a scene where the baseline pipeline fails entirely. These results suggest that proprioceptive data, already computed onboard for gait control, constitutes a lightweight and effective vertical anchor for SLAM in GNSS-denied settings.

Problem

• GNSS-denied
• legged robots
• elevation drift
• SLAM
• LiDAR

Innovation

• leg odometry
• factor graph
• elevation drift
• proprioceptive sensing
• LIO-SAM