  Autonomous exploration in unknown environments using mobile robots is the
pillar of many robotic applications. Existing exploration frameworks either
select the nearest geometric frontier or the nearest information-theoretic
frontier. However, just because a frontier itself is informative does not
necessarily mean that the robot will be in an informative area after reaching
that frontier. To fill this gap, we propose to use a multi-objective variant of
Monte-Carlo tree search that provides a non-myopic Pareto optimal action
sequence leading the robot to a frontier with the greatest extent of unknown
area uncovering. We also adopted Bayesian Hilbert Map (BHM) for continuous
occupancy mapping and made it more applicable to real-time tasks.
