Spatial Reasoning about Relative Orientation and Distance for Robot Exploration

Spatial agents often have to infer global knowledge from local knowledge about orientations and distances. Thereby, the local knowledge based on sensor data is typically imprecise. We propose an approach that propagates orientation and distance intervals to produce global knowledge and compare it with qualitative reasoning calculi in the context of indoor mobile robot exploration. We combine this propagation method with a path-based and predominantly topological mapping approach and demonstrate how it can be utilized to solve the cycle detection problem.