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%.
翻译:自主室内制图问题得到了解决。目标是尽可能缩短时间,以某种预期的确定性达到预定的暴露百分比。提议在路径规划和地图构造中使用预先训练的基因深神经网络,作为地图预测器,以加快绘图过程。这种方法与基于边界的两个不同的地平线数据集路径规划员一起研究。对综合地图预测仪的若干配置进行模拟,其结果显示,通过利用预测,可以大大减少绘图时间。当预测被纳入路径规划和地图构造过程时,显示在某些情况下,绘图时间可能减少50%以上。