Enhancing Graph-Based SLAM in GNSS-Denied environments by leveraging leg odometry
For legged robots operating in GNSS-denied environments, this work provides a lightweight method to mitigate vertical drift in LiDAR-based SLAM.
The paper presents a factor graph architecture that augments LIO-SAM with leg odometry to reduce elevation drift in GNSS-denied environments. On a quadruped robot, elevation drift was reduced from over 30m to under 30cm, and convergence was achieved where the baseline failed.
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.