Abstract
With the widespread deployment of quadcopters, the flight safety issue attracts increasingly public and academic attentions. This article presents a quadcopter flight regime extraction algorithm for quadcopter localization and health monitoring using imageries captured by general purpose monocular cameras. First, contour information is extracted from quadcopter shadows on the ground. In order to better illustrate the three-dimensional silhouette information contained in shadow contour on the ground, a virtual sensor named Shadow Projection Tunnel is designed. Then, multiple Shadow Projection Tunnels are generated according to the extracted silhouette information and corresponding light source positions. Finally, three-dimensional quadcopter positions and flight regimes are extracted based on the aggregation between multiple Shadow Projection Tunnels. The proposed method is validated to be accurate and efficient in monitoring quadcopter position and flight regimes based on the comparative analyses. In comparison with traditional quadcopter health monitoring methods, the proposed method has advantages on deployment convenience, system robustness, precision expandability, and scenario adaptability, making it an ideal solution for quadcopter monitoring in outdoor scenarios.
Highlights
The flourishing development of quadcopter platform contributes enormously to promising fields including drone light shows, intelligent surveillance, and virtual reality
Based on the shadow information, the TX1 embedded system extracts quadcopter parameters frame by frame, outputting the quadcopter location and flight regimes to the embedded flash memory
Based on the precise quadcopter flight regime extraction, the proposed method is superior to the previous external sensor–based methods restricted in quadcopter localization
Summary
The flourishing development of quadcopter platform contributes enormously to promising fields including drone light shows, intelligent surveillance, and virtual reality. Various methods have been proposed to enhance the monitoring technology for quadcopters based on quadcopter onboard sensors or external sensor networks. In the second category of methods, external sensors including Carnegie Mellon University camera (CMUcam), infrared (IR) camera, and ultra-wideband (UWB) transceivers are deployed in the form of sensor network to fulfill the real-time quadcopter monitoring tasks. The proposed method is capable of localizing the quadcopter position and acquiring the flight regimes simultaneously, offering a high-performance quadcopter health monitoring system at a low deployment complexity. In section ‘‘The proposed method,’’ the basic theory for shadow-based single-frame quadcopter localization is introduced first, followed by the quaternion extraction method. In section ‘‘Quadcopter flight status monitoring,’’ the precision of quadcopter localization and flight regime extraction results based on the proposed method is evaluated in comparison with the onboard IMU statistics
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