Abstract
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.
Highlights
Driven by the advantages of on-demand deployment, high mobility, and low cost, unmanned aerial vehicles (UAVs) have become appealing solutions for a wide range of commercial and civilian applications over the past few years, including remote sensing [1], search and rescue [2], and surveillance [3]
The simulation results on the AirSim simulator (The AirSim simulator is a photorealistic game engine with cutting-edge graphics features such as high-resolution textures and realistic lighting and shadows, where the depth image can be obtained in real time) and the experiment results on the developed drone platform are presented in Sections 6.1 and 6.2, respectively
The proposed obstacle surface adaptive plane extraction (OSAPE) scheme is compared with the 3D prisms (3DP) scheme [25] in terms of storage, precision, and processing time
Summary
Driven by the advantages of on-demand deployment, high mobility, and low cost, unmanned aerial vehicles (UAVs) have become appealing solutions for a wide range of commercial and civilian applications over the past few years, including remote sensing [1], search and rescue [2], and surveillance [3]. The resolution of the corresponding grid maps has to be carefully designed, since a small resolution grid consumes a large storage space while a large resolution grid may lead to unfavorable errors near the obstacle surfaces [11] With this consideration, an accurate and compact environment representation model without relying on the grid map needs be developed, so as to provide a concise spatial relationship [12,13] and improve the efficiency of navigation [14], Sensors 2020, 20, 4976; doi:10.3390/s20174976 www.mdpi.com/journal/sensors. To speed up the elimination of irrelevant environmental details, a two-stage plane adjustment algorithm is presented to quickly fill the irrelevant gaps and obtain a rectangular outline of the obstacle
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