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

• URL: http://arxiv.org/abs/2605.20484v1
• Date: Tue, 19 May 2026 20:48:35 GMT
• Title: Enhancing Graph-Based SLAM in GNSS-Denied environments by leveraging leg odometry
• Abstract summary: We present a factor graph architecture that augments the LIO-SAM framework with a parallel kinematic lane driven by leg odometry.<n>Our approach reduces elevation drift from over 30m to under 30cm and enables convergence in a scene where the baseline pipeline fails entirely.
• Score: 2.953334250276907
• License: http://creativecommons.org/licenses/by-sa/4.0/
• 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.

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