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Efficient Environment Mapping Using a Commodity Millimeter-Wave Robot
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DOI：10.1109/TWC.2022.3229661.png)
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摘要
En 中文
Ambient environment information, including reflectors' geometrical layout, dimension, and reflectivity, is a key input to versatile millimeter-wave networking and sensing applications. It has found versatile applications in optimizing network coverage and robustness, enhancing mobile link performance, and enabling high-accuracy indoor localization and navigation. Recent approaches of deriving mmWave environment information require heavy infrastructure support and rely on costly software-defined radios, which prevent their usage in practice. In this work, we design and implement e-mmRanger, which can efficiently sense the environment without infrastructure support. e-mmRanger equips a pair of low-cost off-the-shelf mmWave radios in a commodity robot, which constantly samples the ambient environment by exchanging a series of mmWave signals while it moves. It then re-engineers the time-domain signal series to derive the spatial-domain environment structure through novel reflection path extraction and clustering algorithms. Moreover, e-mmRanger accelerates the mapping process by incorporating a novel Space-knit algorithm, which strategically plans an optimal movement route consisting of minimum sampling locations for the robot. Our experiments verify that e-mmRanger can accurately and efficiently sense the surrounding environment, and the learned information can bring multi-fold performance gain over empirical approaches in mmWave networks.
Keyword:
Millimeter wave communication
Robot sensing systems
Wireless communication
Robot kinematics
Clustering algorithms
Wireless sensor networks
Sensors
Environment mapping
millimeter wave
reflection path extraction and clustering
mapping acceleration
期刊
IF:
10.7
论文数:
1.3W
被引数:
5.3W
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