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Estimating map completeness in robot exploration

delete2025-12-26
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OA
AI
M
Matteo Luperto
M
Marco Maria Ferrara
M
Matteo Princisgh
G
Giacomo Boracchi
F
Francesco Amigoni
DOI：10.1007/s10514-025-10221-8delete
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摘要

摘要

En 中文
我们提出了一种新颖的方法，该方法在给定部分探索的室内环境的网格地图时，能够估计地图中已探索区域的比例，并判断是否值得继续探索环境中未覆盖的部分。我们的方法基于以下理念：现代深度学习模型可以通过利用地图中的视觉线索成功解决此任务。因此，我们在描绘来自部分探索环境的网格地图的图像上训练了一个深度卷积神经网络，其标注信息来源于对完整地图的了解，而该信息在神经网络用于推理时是不可用的。我们证明，我们的网络可以用于定义一个停止准则，在预期探索过程不再为地图添加关于环境的详细信息时成功终止探索过程，与覆盖整个环境区域相比，节省了超过35%的总探索时间。
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期刊

Autonomous Robots 封面图
Autonomous Robots
IF:
4.3
论文数:
1.7K
被引数:
5.0K

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