Occlusion-Robust Relative Pose Estimation for Multi-Robot Systems Via Geometric-Aware Diffusion Matching

Abstract

Relative pose estimation is crucial for coordinated multi-robot navigation. However, robots in close proximity often face intra-team occlusions, where teammates partially block each other's field of view, while dynamic environments further introduce environmental occlusions. Classical relative pose estimation methods degrade under occlusion and texture scarcity, whereas learning-based methods often lack explicit geometric consistency, which limits their accuracy during real deployments. To address multi-robot relative pose estimation in complex 3D environments, we introduce Geometric-Aware Diffusion Matching (GADM), which enables a team of robots to estimate relative 6-DoF poses using only RGB-D sensors, even under occlusions. GADM uses a diffusion model to progressively exploit global and higher-order structural constraints encoded by a graph network, guiding smoother optimization and faster convergence to robust correspondence distributions under noise and occlusions. By integrating geometric consistency, GADM explicitly addresses occlusions by producing geometrically consistent matches suitable for real-time deployment on physical robots. The resulting correspondences are then used with geometry-based solvers to estimate 6-DoF relative poses, providing robustness even under partial view overlap and limited keypoint visibility. We conducted experiments using both robotics simulations and physical robot teams, and our results show that GADM achieves robust 6-DoF pose estimation performance in occluded scenarios.