深度强化与监督学习相结合加速室内测绘
Integrating Deep Reinforcement and Supervised Learning to Expedite Indoor Mapping
摘要：
解决了绘制室内环境的挑战。求解运动规划问题的典型启发式算法是基于前沿的方法，这在环境完全未知时特别有效。然而，在关于环境的架构特征的先前统计数据可用的情况下，这样的算法可能远非最佳。此外，它们的计算时间可能随着更多的区域被暴露而显著增加。在本文中，我们提出了两种方法来克服这些缺点。一个是使用深度强化学习来训练运动规划器。第二个是包含一个预训练的生成式深度神经网络，用作地图预测器。每一个都有助于通过使用环境的学习结构统计来改进决策，并且两者都被实现为神经网络，确保恒定的计算时间。我们表明，结合这两种方法可以缩短映射过程的持续时间高达4倍，相比基于前沿的运动规划。
英文摘要：
The challenge of mapping indoor environments is addressed. Typical heuristic algorithms for solving the motion planning problem are frontier-based methods, that are especially effective when the environment is completely unknown. However, in cases where prior statistical data on the environment's architectonic features is available, such algorithms can be far from optimal. Furthermore, their calculation time may increase substantially as more areas are exposed. In this paper we propose two means by which to overcome these shortcomings. One is the use of deep reinforcement learning to train the motion planner. The second is the inclusion of a pre-trained generative deep neural network, acting as a map predictor. Each one helps to improve the decision making through use of the learned structural statistics of the environment, and both, being realized as neural networks, ensure a constant calculation time. We show that combining the two methods can shorten the duration of the mapping process by up to 4 times, compared to frontier-based motion planning.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
