LONG–TERM AUTONOMY OF MOBILE ROBOTS IN CHANGING ENVIRONMENTS

Abstract

This habilitation thesis presents research that aims to enable long-term deployment of mobile robots in changing environments. The presented approaches encompass methods that ensure robustness of autonomous visual navigation in outdoor environments for prolonged time periods, spatio-temporal representations that explicitly model the environment changes over time, and supporting software modules that enable robust and accurate robot localisation. The main contribution of the thesis is a novel approach that allows to incorporate the notion of time into most stationary environment models used in mobile robotics. This is achieved by representing the uncertainty of the environment states not by fixed probabilities, but by probabilistic functions of time, represented in the frequency domain. The method allows to integrate unlimited numbers of sparse and irregular observations obtained during long-term deployments of mobile robots into memory-efficient models that reflect the persistence and recurrence of environment variations. The frequency-enhanced spatio-temporal models allow to predict the future environment states, which improves the efficiency of mobile robot operation in changing environments. In this thesis, we present a series of articles, which demonstrate that the proposed approach improves mobile robot localization, path and task planning, activity recognition, human-robot interaction and allows for life-long spatio-temporal exploration of perpetually-changing environments.