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SIGS: Synthetic Imagery Generating Software for the Development and Evaluation of Vision-based Sense-And-Avoid Systems

Abstract

Unmanned Aerial Systems (UASs) have recently become a versatile platform for many civilian applications including inspection, surveillance and mapping. Sense-and-Avoid systems are essential for the autonomous safe operation of these systems in non-segregated airspaces. Vision-based Sense-and-Avoid systems are preferred to other alternatives as their price, physical dimensions and weight are more suitable for small and medium-sized UASs, but obtaining real flight imagery of potential collision scenarios is hard and dangerous, which complicates the development of Vision-based detection and tracking algorithms. For this purpose, user-friendly software for synthetic imagery generation has been developed, allowing to blend user-defined flight imagery of a simulated aircraft with real flight scenario images to produce realistic images with ground truth annotations. These are extremely useful for the development and benchmarking of Vision-based detection and tracking algorithms at a much lower cost and risk. An image processing algorithm has also been developed for automatic detection of the occlusions caused by certain parts of the UAV which carries the camera. The detected occlusions can later be used by our software to simulate the occlusions due to the UAV that would appear in a real flight with the same camera setup. Additionally this algorithm could be used to mask out pixels which do not contain relevant information of the scene for the visual detection, making the image search process more efficient. Finally an application example of the imagery obtained with our software for the benchmarking of a state-of-art visual tracker is presented.

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Carrio, A., Fu, C., Collumeau, JF. et al. SIGS: Synthetic Imagery Generating Software for the Development and Evaluation of Vision-based Sense-And-Avoid Systems. J Intell Robot Syst 84, 559–574 (2016). https://doi.org/10.1007/s10846-015-0286-z

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  • DOI: https://doi.org/10.1007/s10846-015-0286-z

Keywords

  • Sense-And-Avoid
  • Collision avoidance
  • UAV