Autonomous exploration is a crucial aspect of robotics, enabling robots to explore unknown environments and generate maps without prior knowledge. This paper proposes a method to enhance exploration efficiency by integrating neural network-based occupancy grid map prediction with uncertainty-aware Bayesian neural network. Uncertainty from neural network-based occupancy grid map prediction is probabilistically integrated into mutual information for exploration. To demonstrate the effectiveness of the proposed method, we conducted comparative simulations within a frontier exploration framework in a realistic simulator environment against various information metrics. The proposed method showed superior performance in terms of exploration efficiency.
@article{arxiv.2412.12825,
title = {Enhancing Exploration Efficiency using Uncertainty-Aware Information Prediction},
author = {Seunghwan Kim and Heejung Shin and Gaeun Yim and Changseung Kim and Hyondong Oh},
journal= {arXiv preprint arXiv:2412.12825},
year = {2024}
}