Information-theoretic Frontier Selection for Environment Exploration

The exploration of unknown environments using autonomous mobile robots is essential for different applications, for example, search and rescue missions. The main objective is to efficiently transverse the environment and build a complete and accurate map. However, different applications may demand different exploration strategies. The simplest strategy is a greedy approach which visits the closest frontier without considering if it will yield a significant reduction in map uncertainty. In this paper, we propose a novel method to predict information beyond the candidate frontiers by analyzing the local structure. Next, the utility function chooses a candidate locations using Shannon entropy. The methodology was evaluated through several experiments in a simulated environment, showing that our exploration approach is better suited for rapid exploration than the classic Near-Frontier Exploration (NFE).