Lifelong Information-Driven Exploration to Complete and Refine 4-D Spatio-Temporal Maps

This letter presents an exploration method that allows mobile robots to build and maintain spatio-temporal models of changing environments. The assumption of a perpetually changing world adds a temporal dimension to the exploration problem, making spatio-temporal exploration a never-ending, life-long learning process. We address the problem by application of information-theoretic exploration methods to spatio-temporal models that represent the uncertainty of environment states as probabilistic functions of time. This allows to predict the potential information gain to be obtained by observing a particular area at a given time, and consequently, to decide which locations to visit and the best times to go there. To validate the approach, a mobile robot was deployed continuously over 5 consecutive business days in a busy office environment. The results indicate that the robot's ability to spot environmental changes improved as it refined its knowledge of the world dynamics.