A framework of probabilistic 3D map fusion for collaborative robots - Yue, Yufeng (2019)
Utilizing multi-robot systems for environmental mapping, inspection or surveillance tasks have found significant attention over the past few years due to the inherent advantages of robustness and efficiency. Due to its limited sensing capabilities and computational power, each robot only has partial information of the surroundings. To maintain a comprehensive understanding of the environment under limited communication bandwidth, fusing local 3D maps generated by individual robots to a globally consistent map is a critical challenge in multi-robot mapping missions. Most of the existing approaches to collaborative mapping are subject to certain challenges such as uncertainty modeling, estimating relative transformation, information integrating, inconsistency detection, and communication management. This thesis proposes a systematic probabilistic multi-robot mapping framework that takes these challenges into account.