Cyrill Stachniss

Autonomous navigation is an essential capability for mobile robots. In order to operate robustly, a robot needs to know what the environment looks like, where it is in its environment, and how to navigate within it. Particle Filters for Robot Navigation summarizes approaches that address these three problems and that use particle filters as their main underlying model for representing beliefs. It illustrates that these filters are powerful tools that can robustly estimate the state of the robot and its environment and that they are also well-suited to making decisions about how to navigate in order to minimize a robot's uncertainty about its own position and the state of the environment. They offer a series of attractive capabilities, including the ability to deal with non-Gaussian distributions and nonlinear sensor and motion models. Particle filters have been used for almost twenty years in robotics and have become a standard means for a series of tasks. This is an ideal primer for anyone interested in the research and application of these filters in robotics.

"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.

"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.