Localization and Navigation

Localization and navigation are the two most important tasks for mobile robots. We want to know where we are, and we need to be able to make a plan for how to reach a goal destination. Of course these two problems are not isolated from each other, but rather closely linked. If a robot does not know its exact position at the start of a planned trajectory, it will encounter problems in reaching the destination.

After a variety of algorithmic approaches were proposed in the past for localization, navigation, and mapping, probabilistic methods that minimize uncertainty are now applied to the whole problem complex at once (e.g. SLAM, simultaneous localization and mapping).

In this Chapter, we will look at navigation algorithms that operate with or without maps. A navigation algorithm without a map (e.g. DistBug) is often used in a continuously changing environment or if a path has to be traveled only once and therefore does not necessarily have to be optimal. If a map is provided, then algorithms like Dijkstra or A* can be applied to find the shortest path offline before the robot starts driving. Navigation algorithms without maps operate in direct interaction with the robot’s sensors while driving. Navigation algorithms with maps require a nodal distance graph that has to be either provided or needs to be extracted from the environment (e.g. Quadtree method).