Real-Time Compact Environment Representation for UAV Navigation
1
Electronic Information School, Wuhan University, Wuhan 430072, China
2
Collaborative Innovation Center of Geospatial Technology, Wuhan 430079, China
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(17), 4976; https://doi.org/10.3390/s20174976
Received: 24 July 2020
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Revised: 17 August 2020
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Accepted: 25 August 2020
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Published: 2 September 2020
(This article belongs to the Special Issue Sensors and Perception Systems for Mobile Robot Navigation)
Recently, unmanned aerial vehicles (UAVs) have attracted much attention due to their on-demand deployment, high mobility, and low cost. For UAVs navigating in an unknown environment, efficient environment representation is needed due to the storage limitation of the UAVs. Nonetheless, building an accurate and compact environment representation model is highly non-trivial because of the unknown shape of the obstacles and the time-consuming operations such as finding and eliminating the environmental details. To overcome these challenges, a novel vertical strip extraction algorithm is proposed to analyze the probability density function characteristics of the normalized disparity value and segment the obstacles through an adaptive size sliding window. In addition, a plane adjustment algorithm is proposed to represent the obstacle surfaces as polygonal prism profiles while minimizing the redundant obstacle information. By combining these two proposed algorithms, the depth sensor data can be converted into the multi-layer polygonal prism models in real time. Besides, a drone platform equipped with a depth sensor is developed to build the compact environment representation models in the real world. Experimental results demonstrate that the proposed scheme achieves better performance in terms of precision and storage as compared to the baseline.
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Keywords:
unmanned aerial vehicle; obstacle sensing; compact environment representation; kernel density estimation
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MDPI and ACS Style
Meng , K.; Li , D.; He , X.; Liu , M.; Song , W. Real-Time Compact Environment Representation for UAV Navigation. Sensors 2020, 20, 4976. https://doi.org/10.3390/s20174976
AMA Style
Meng K, Li D, He X, Liu M, Song W. Real-Time Compact Environment Representation for UAV Navigation. Sensors. 2020; 20(17):4976. https://doi.org/10.3390/s20174976
Chicago/Turabian StyleMeng , Kaitao, Deshi Li , Xiaofan He , Mingliu Liu , and Weitao Song . 2020. "Real-Time Compact Environment Representation for UAV Navigation" Sensors 20, no. 17: 4976. https://doi.org/10.3390/s20174976
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