Paper
8 June 2011 Drogue tracking using 3D flash lidar for autonomous aerial refueling
Chao-I Chen, Roger Stettner
Author Affiliations +
Abstract
Autonomous aerial refueling (AAR) is an important capability for an unmanned aerial vehicle (UAV) to increase its flying range and endurance without increasing its size. This paper presents a novel tracking method that utilizes both 2D intensity and 3D point-cloud data acquired with a 3D Flash LIDAR sensor to establish relative position and orientation between the receiver vehicle and drogue during an aerial refueling process. Unlike classic, vision-based sensors, a 3D Flash LIDAR sensor can provide 3D point-cloud data in real time without motion blur, in the day or night, and is capable of imaging through fog and clouds. The proposed method segments out the drogue through 2D analysis and estimates the center of the drogue from 3D point-cloud data for flight trajectory determination. A level-set front propagation routine is first employed to identify the target of interest and establish its silhouette information. Sufficient domain knowledge, such as the size of the drogue and the expected operable distance, is integrated into our approach to quickly eliminate unlikely target candidates. A statistical analysis along with a random sample consensus (RANSAC) is performed on the target to reduce noise and estimate the center of the drogue after all 3D points on the drogue are identified. The estimated center and drogue silhouette serve as the seed points to efficiently locate the target in the next frame.
© (2011) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Chao-I Chen and Roger Stettner "Drogue tracking using 3D flash lidar for autonomous aerial refueling", Proc. SPIE 8037, Laser Radar Technology and Applications XVI, 80370Q (8 June 2011); https://doi.org/10.1117/12.886572
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CITATIONS
Cited by 25 scholarly publications and 16 patents.
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KEYWORDS
Cameras

3D acquisition

LIDAR

Target detection

Detection and tracking algorithms

Image segmentation

Target recognition

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