Online Exploration of Tunnel Networks Leveraging Topological CNN-based World Predictions
Online Exploration of Tunnel Networks Leveraging Topological CNN-based World Predictions
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DOI：
10.1109/iros45743.2020.9341170
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发表时间：
2020-10
期刊：
影响因子：
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通讯作者：
Manish Saroya;Graeme Best;Geoffrey A. Hollinger
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文献类型：
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作者：
Manish Saroya;Graeme Best;Geoffrey A. Hollinger
机器人探索需要自适应地选择导航目标，从而快速发现和绘制未知世界。在许多现实世界的环境中，微妙的结构线索可以提供关于未探索的世界的洞察力，这可以被决策者利用来提高探索的速度。在稀疏的地下隧道网络中，这些线索以拓扑特征的形式出现，例如环路或死胡同，这些特征通常在相似的环境中很常见。我们提出了一种学习这些拓扑特征的方法，利用从拓扑图像分割和图像修复中借来的技术，从世界数据库中学习。然后，这些世界预测为基于前沿的勘探政策提供信息。我们的模拟实验与一组真实世界的矿山环境和程序生成的人工隧道网络的数据库表明，探索的区域的速率大幅增加相比，不试图预测和利用未探索的世界的拓扑特征的技术。
Robotic exploration requires adaptively selecting navigation goals that result in the rapid discovery and mapping of an unknown world. In many real-world environments, subtle structural cues can provide insight about the unexplored world, which may be exploited by a decision maker to improve the speed of exploration. In sparse subterranean tunnel networks, these cues come in the form of topological features, such as loops or dead-ends, that are often common across similar environments. We propose a method for learning these topological features using techniques borrowed from topological image segmentation and image inpainting to learn from a database of worlds. These world predictions then inform a frontier-based exploration policy. Our simulated experiments with a set of real-world mine environments and a database of procedurally-generated artificial tunnel networks demonstrate a substantial increase in the rate of area explored compared to techniques that do not attempt to predict and exploit topological features of the unexplored world.