Probabilistic Relative Pose Calibration for Object-Level Multi-Agent Cooperative Perception

Hailiang Zhang, Zhiying Song, Fuxi Wen

Abstract: Online relative pose estimation within constrained time frame is a critical challenge for object-level multi-agent cooperative perception. Specifically, its objective is to determine the relative translation and rotation of cooperating agents such that the detected objects are aligned. Current methodologies adopt a non-probabilistic approach to data association and a singular association hypothesis is assumed, resulting in overconfident pose estimates and diminished accuracy in ambiguous environments. A probabilistic relative pose estimation approach is proposed to directly address this limitation by jointly considering all the potential association hypotheses and their respective likelihoods. We construct a comprehensive Bayesian estimation problem encompassing data association and pose inference. An iterative message-passing algorithm is employed on the proposed factor graph to derive near-optimal and real-time relative pose estimates. Numerical studies verify the real-time performance and effectiveness of the proposed method.