深度学习辅助路径规划和地图构建以加快室内测绘
Deep-Learning-Aided Path Planning and Map Construction for Expediting Indoor Mapping
摘要：
解决了自主室内测绘的问题。目标是最大限度地减少达到预定义的暴露百分比的时间，并具有某些所需的确定性水平。为了加快映射过程，提出了在路径规划和地图构建中使用预训练的生成式深度神经网络作为地图预测器。结合两个不同的布图规划数据集的几个基于边界的路径规划器，该方法进行了检查。模拟运行几种配置的集成映射预测器，其结果表明，通过利用预测映射时间的显着减少是可能的。当预测集成在路径规划和地图构建过程中，它表明，在某些情况下，映射时间可以减少超过50%。
英文摘要：
The problem of autonomous indoor mapping is addressed. The goal is to minimize the time to achieve a predefined percentage of exposure with some desired level of certainty. The use of a pre-trained generative deep neural network, acting as a map predictor, in both the path planning and the map construction is proposed in order to expedite the mapping process. This method is examined in combination with several frontier-based path planners for two distinct floorplan datasets. Simulations are run for several configurations of the integrated map predictor, the results of which reveal that by utilizing the prediction a significant reduction in mapping time is possible. When the prediction is integrated in both path planning and map construction processes it is shown that the mapping time may in some cases be cut by over 50%.
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。
