This book examines simultaneous localization and mapping (SLAM) for autonomous robots. Solutions include uncertainty-driven exploration, coordination of multiple robots, learning and incorporating background knowledge, and dealing with dynamic environments.

"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.

Recent research in the area of self-localization and mapping (SLAM) for autonomous robots The presented solutions include uncertainty-driven exploration, active loop closing, coordination of multiple robots, learning and incorporating background knowledge, and dealing with dynamic environments. accompanied by a rich set of experiments

Inhalt
Basic 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.