This work examines the challenges of distributed map merging and localization in multi-robot systems, which enables robots to acquire the knowledge of their surroundings needed to carry out coordinated tasks. After identifying the main issues associated with this problem, each chapter introduces a different distributed strategy for solving them.
In addition to presenting a review of distributed algorithms for perception in localization and map merging, the text also provides the reader with the necessary tools for proposing new solutions to problems of multi-robot perception, as well as other interesting topics related to multi-robot scenarios.
This work will be of interest to postgraduate students and researchers in the robotics and control communities, and will appeal to anyone with a general interest in multi-robot systems. The reader will not require any prior background knowledge, other than a basic understanding of mathematics at a graduate-student level. The coverage is largely self-contained, supported by numerous explanations and demonstrations, although references for further study are also supplied.Inhalt
Introduction.- Distributed Data Association.- Distributed Localization.- Map Merging.- Real Experiments.- Conclusions.- Appendix A: Averaging Algorithms and Metropolis Weights.- Appendix B: Auxiliary Results for Distributed Localization.