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arXiv · 2609.20624

SmellDiffusion: Diffusion-Based Quadruped Navigation with Olfactory Scene Graphs

Faith Ogunwoye·Iana Zhura·Hajira Amjad·Timofei Kozlov·Didar Seyidov·Dmitrii Plotnikov·Fedor Fedorov·Dzmitry Tsetserukou
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Abstract

A robot sent to a named gas leak must preserve gas identity, estimate the source, and navigate to the resulting goal. We present SmellDiffusion, a simulation pipeline that represents species-specific gas zones in an open-vocabulary olfactory scene graph and shares the selected goal between classical and diffusion planners. Its key components are a peak-local geometric gate for selective source correction and diffusion-based, gas-guided trajectory generation. Among 424 unique source-wind configurations in solved flow, 28 have a concentration peak displaced more than 0.5m from the source. A source-independent geometric gate, calibrated only on the training split and evaluated at the observed peak, detects 9 of 10 held-out displacements at 0.64 precision. Gating a precomputed forward-matching correction reduces mean error on the displaced cases from 1.468m to 0.592m (60%), using matching for only 14/204 cases. All-case mean error falls from 0.205m to 0.180m. All planners receive the same scene-graph source estimate as their goal. In a controlled comparison, best-of-ten diffusion achieves mean gas exposure comparable to gas-guided A* (0.0476 versus 0.0455). A single diffusion proposal takes 41.7ms, compared with 72.3ms for gas-guided A*, although best-of-ten sequential sampling increases total runtime. Plain A* also reaches the same goal and remains the fastest and shortest-path method. Six matched Gazebo runs give mean robot-to-source errors of 0.39m for A* and 0.31m for diffusion.

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Faith Ogunwoye, Iana Zhura, Hajira Amjad, Timofei Kozlov, Didar Seyidov, Dmitrii Plotnikov, Fedor Fedorov, Dzmitry Tsetserukou. 2026-09-18. SmellDiffusion: Diffusion-Based Quadruped Navigation with Olfactory Scene Graphs. https://arxiv.org/abs/2609.20624

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