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arXiv · 2412.12825

Enhancing Exploration Efficiency using Uncertainty-Aware Information Prediction

Seunghwan Kim·Heejung Shin·Gaeun Yim·Changseung Kim·Hyondong Oh
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Abstract

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.

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Seunghwan Kim, Heejung Shin, Gaeun Yim, Changseung Kim, Hyondong Oh. 2024-12-17. Enhancing Exploration Efficiency using Uncertainty-Aware Information Prediction. https://arxiv.org/abs/2412.12825

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