Probabilistic 3D Semantic Map Fusion Based on Bayesian Rule

Abstract

Performing collaborative semantic mapping is a critical challenge for multi-robot systems to maintain a comprehensive contextual understanding of the surroundings. In this paper, a novel hierarchical semantic map fusion framework is proposed, where the problem is addressed in low-level single robot semantic mapping and high level global semantic map fusion. In the single robot semantic mapping process, Bayesian rule is used for label fusion and occupancy probability updating, where the semantic information is added to the geometric map grid. High level global semantic map fusion covers decentralized map sharing and global semantic map updating. Collaborative semantic mapping is conducted in two scenarios, that is, NTU dataset and the KITTI dataset. The results show the high quality of the global semantic map, which demonstrates the utility and versatility of 3D semantic map fusion algorithm.