"Robotic Mapping and Exploration" is an important contribution in the area of simultaneous localization and mapping (SLAM) for autonomous robots, which has been receiving a great deal of attention by the research community in the latest few years. The contents are focused on the autonomous mapping learning problem. Solutions include uncertainty-driven exploration, active loop closing, coordination of multiple robots, learning and incorporating background knowledge, and dealing with dynamic environments. Results are accompanied by a rich set of experiments, revealing a promising outlook toward the application to a wide range of mobile robots and field settings, such as search and rescue, transportation tasks, or automated vacuum cleaning.
Table of ContentsBasic Techniques.- I: Exploration with Known Poses.- Decision-Theoretic Exploration Using Coverage Maps.- Coordinated Multi-Robot Exploration.- Multi-Robot Exploration Using Semantic Place Labels.- II: Mapping and Exploration under Pose Uncertainty.- Efficient Techniques for Rao-Blackwellized Mapping.- Actively Closing Loops During Exploration.- Recovering Particle Diversity.- Information Gain-based Exploration.- Mapping and Localization in Non-Static Environments.- Conclusion.