论文标题
深度学习辅助的路径计划和地图构造，用于加快室内映射
Deep-Learning-Aided Path Planning and Map Construction for Expediting Indoor Mapping
论文作者
论文摘要
自动室内映射的问题解决了。目的是最大程度地减少达到预定百分比的暴露百分比的时间，并以一定的确定性水平。在路径计划和地图构造中，都使用预训练的生成深神经网络，作为地图预测指标，以加快映射过程。该方法与两个不同的平面图数据集的几个基于边界的路径计划组合检查。对集成地图预测变量的多种配置进行了仿真，其结果表明，通过利用预测，可以大大减少映射时间。当预测均集成到路径计划和地图构造过程中时，据表明，在某些情况下，映射时间可能会削减50％以上。
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%.
