Route description interpretation on automatically labeled robot maps

This paper presents an approach to combine automatic semantic place labeling of robot-generated maps with reasoning on human route descriptions. Enabling robots to understand human route descriptions can simplify HRI situations in household or industrial settings. However, solving this problem requires handling the ambiguity present in route descriptions and the possible unreliability of the semantic perception capabilities of the robot. We address this problem by absorbing these uncertainties in a probability distribution measuring the likelihood of the different interpretations (paths) of a given route description and selecting its MAP solution. The approach is evaluated on a dataset of route descriptions transcribed into a suitable representation using standard information retrieval metrics. These performance measurements indicate that the method can correctly interpret route descriptions even in challenging environments.