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

TL;DR

This paper introduces a factor graph architecture that combines leg odometry with LiDAR-inertial data to significantly reduce elevation drift in GNSS-denied environments for legged robots.

Contribution

It presents a novel integration of proprioceptive leg odometry into a SLAM framework, improving vertical accuracy in challenging outdoor scenarios.

Findings

Reduced elevation drift from over 30m to under 30cm

Enabled SLAM convergence where baseline failed

Validated on a quadruped platform over 1 km outdoor loops

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.