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

[2605.20484] Enhancing Graph-Based SLAM in GNSS-Denied environments by leveraging leg odometry

[Submitted on 19 May 2026]

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

View a PDF of the paper titled Enhancing Graph-Based SLAM in GNSS-Denied environments by leveraging leg odometry, by L\'eon Perruchot-Triboulet and 2 other authors

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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.

Comments: 4 pages, 3 figures, 2 tables, for ICRA workshop on Robot Meets GNSS and Ranging for Seamless Autonomy

Subjects:

Robotics (cs.RO)

Cite as: arXiv:2605.20484 [cs.RO]

(or arXiv:2605.20484v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2605.20484

arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Léon Perruchot-Triboulet [view email] [v1] Tue, 19 May 2026 20:48:35 UTC (3,261 KB)

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