Information-Based Heuristics for Learned Goal-Driven Exploration and Mapping

In this paper, we propose a novel heuristics function for evaluating and selecting intermediate points in a goal-driven autonomous exploration and mapping system where navigation is performed by a learned neural network. The function calculation takes into consideration the training setting of the deep reinforcement learning-based network and combines it with distance information towards the global goal and the map information. The candidate with the minimum score is selected as the current intermediate goal. This allows the navigation system to be guided towards the global goal in an informed manner. Experiments in simulation and real-world settings show the benefit of the proposed approach over similar heuristic candidate point evaluation methods.