Place learning with a human tracking mobile robot

This thesis is concerned with human-guided spatial cognition. This is an important problem in human-robot interaction as it can yield human-like knowledge of places. It is a difficult problem as it requires the robot to have three capabilities that can function in an integrated manner - namely human tracking, human following, and spatial reasoning while doing so. The goal of tracking is human detection and estimating the relative position of the human as long as s/he remains within the robot's field of view. We consider a robot endowed with an RGB-D sensor and propose an approach that consists of five stages: human detection, target region selection, detection improvement, target distance calculation, and relative position derivation. Human detection is based on one of two alternative existing methods depending on whether the processing power or accuracy is of priority. In case the target cannot be found or if more than one target is found, the continuity of tracking is ensured through position estimation. The goal of human following is to ensure that the robot keeps the target human within its field of view - even if the human is bodily moving. This is achieved using a reactive navigation approach - based on previous work. As such, the robot is able to follow the target while avoiding collisions along the way. The final stage is spatial reasoning. Here, we utilize a topological spatial cognition model. In this model, a place refers to an area with spatial extent as defined by its appearances. The model works in conjunction with a place memory and places are detected, recognized or learned if necessary. Our experimental results indicate that the three capabilities can operate in an integrated manner.