Long-Term Exploration in Unknown Dynamic Environments

The task of exploration does not end when the robot has covered the entire environment. The world is dynamic and to model this property and to keep the map up to date the robot needs to re-explore. In this work, we present an approach to long-term exploration that builds on prior work on dynamic mapping, volumetric representations of space, and exploration planning. The main contribution of our work is a novel formulation of the information gain function that controls the exploration so that it trades off revisiting highly dynamic areas where changes are very likely with covering the rest of the environment to ensure both coverage and up-to-date estimates of the dynamics. We provide experimental validation of our approach in three different simulated environments.