Probabilistic Fusion Framework for Collaborative Robots 3D Mapping

Abstract

Fusion of local 3D maps generated by individual robots to a globally consistent 3D map is one of the fundamental challenges in multi-robot mapping missions. In this paper, we propose a probabilistic mathematical formulation to address the integrated map fusion problem. More specifically, the problem of estimating fused map posterior can be factorized into a product of relative transformation posterior and the global map posterior, which enables us to solve map matching and map merging problems efficiently. In addition, a distributed communication strategy is employed to share map information among robots. The proposed approach is evaluated in indoor and mixed environments, which shows its utility in 3D map fusion for multi-robot mapping missions.