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.

翻译：自主探索是机器人学的关键环节，它使机器人能够在缺乏先验知识的情况下探索未知环境并构建地图。本文提出一种通过将基于神经网络的占据栅格地图预测与不确定性感知贝叶斯神经网络相结合来提升探索效率的方法。该方法将基于神经网络的占据栅格地图预测产生的不确定性以概率形式整合至用于探索的互信息中。为验证所提方法的有效性，我们在真实仿真环境的前沿探索框架内，针对多种信息度量指标进行了对比仿真实验。实验结果表明，所提方法在探索效率方面具有优越性能。