Abstract
An unmanned aircraft system (UAS) is an aircraft without a human pilot onboard, controlled either by a ground-based controller or with a pre-set flight plan. With the rise of artificial intelligence (AI), people have become increasingly interested to enable the UAS to navigate on its own, to make autonomous decisions for its flight path while carrying out certain tasks rather than simply executing a pre-set flight path. This requires the UAS to have a certain awareness of its location in space and the obstacles around it. Hence, additional sensors must be mounted on the UAS to give it vision and awareness of its surroundings. However, the payload is still preferably kept to the minimum for better overall efficiency of flight. Here we explore the possibility of reducing payload by combining sensing and navigating tasks together. Specifically, we design the vision-based navigation system to be able to do 3D nominal scene reconstruction through video input from a single onboard 2D camera for object localization and navigation. A nominal scene reconstruction will be sufficient for UAS navigation as it only has to be aware of its proximity to corners and surfaces, to be able to avoid them and not collide into obstructions. Initial works show promising results, i.e. the system can produce 3D point clouds (nominal scene & geometry) from video stream provided by on-board camera.
This research is supported in part by Defense Science and Technology Agency (DSTA).
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References
Agisoft: Agisoft metashape (2019). https://www.agisoft.com/. Accessed 23 Dec 2020
Intel: Intel realsense (2020). https://www.intel.sg/content/www/xa/en/architecture-and-technology/realsense-overview.html. Accessed 25 Dec 2020
Kim, H., Leutenegger, S., Davison, A.J.: Real-time 3D reconstruction and 6-DOF tracking with an event camera. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9910, pp. 349–364. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46466-4_21
Koch, T., Körner, M., Fraundorfer, F.: Automatic and semantically-aware 3D UAV flight planning for image-based 3D reconstruction. Remote Sens. 11(13), 1550 (2019)
MathWorks: Structure from motion from multiple views (2019). https://www.mathworks.com/help/vision/ug/structure-from-motion-from-multiple-views.html. Accessed 23 Dec 2020
Meshlab: Mesh lab open source for processing and editing 3D triangular meshes (2020). https://www.meshlab.net/. Accessed 25 Dec 2020
Microsoft: Microsoft kinect (2020). https://www.microsoft.com/en-us/p/azure-kinect-dk. Accessed 25 Dec 2020
Stereolabs: Stereolabs zed cameras (2020). https://www.stereolabs.com/zed/. Accessed 25 Dec 2020
Xu, Z., Wu, L., Gerke, M., Wang, R., Yang, H.: Skeletal camera network embedded structure-from-motion for 3D scene reconstruction from UAV images. ISPRS J. Photogramm. Remote. Sens. 121, 113–127 (2016)
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Han, X., Srigrarom, S. (2022). 3D Nominal Scene Reconstruction for Object Localization and UAS Navigation. In: Ang Jr, M.H., Asama, H., Lin, W., Foong, S. (eds) Intelligent Autonomous Systems 16. IAS 2021. Lecture Notes in Networks and Systems, vol 412. Springer, Cham. https://doi.org/10.1007/978-3-030-95892-3_6
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