Loops are pervasive in robotics problems, appearing in mapping and localization, where one is interested in finding loop closure constraints to better approximate robot poses or other estimated quantities, as well as planning and prediction, where one is interested in the homotopy classes of the space through which a robot is moving. We generalize the standard topological definition of a loop to cases where a trajectory passes close to itself, but doesn’t necessarily touch, giving a definition that is more practical for real robotics problems. This relaxation leads to new and useful properties of inexact loops, such as their ability to be partitioned into topologically connected sets closely matching the concept of a “loop closure”, and the existence of simple and nonsimple loops. Building from these ideas, we introduce several ways to measure properties and quantities of inexact loops on a trajectory, such as the trajectory’s “loop area” and “loop density’”, and use them to compare strategies for sampling representative inexact loops to build constraints in mapping and localization problems.
