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Large scale monocular vision-only mapping from a fixed-wing sUAS

Warren, Michael, McKinnon, David, He, Hu, Glover, Arren, Shiel, Michael, & Upcroft, Ben (2014) Large scale monocular vision-only mapping from a fixed-wing sUAS. In Field and Service Robotics: Results of the 8th International Conference [Springer Tracts in Advanced Robotics, Volume 92], Springer, Matsushima, Japan, pp. 495-509.

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Abstract

This paper presents the application of a monocular visual SLAMon a fixed-wing small Unmanned Aerial System (sUAS) capable of simultaneous estimation of aircraft pose and scene structure. We demonstrate the robustness of unconstrained vision alone in producing reliable pose estimates of a sUAS, at altitude. It is ultimately capable of online state estimation feedback for aircraft control and next-best-view estimation for complete map coverage without the use of additional sensors.We explore some of the challenges of visual SLAM from a sUAS including dealing with planar structure, distant scenes and noisy observations. The developed techniques are applied on vision data gathered from a fast-moving fixed-wing radio control aircraft flown over a 1×1km rural area at an altitude of 20-100m.We present both raw Structure from Motion results and a SLAM solution that includes FAB-MAP based loop-closures and graph-optimised pose. Timing information is also presented to demonstrate near online capabilities. We compare the accuracy of the 6-DOF pose estimates to an off-the-shelfGPS aided INS over a 1.7kmtrajectory.We also present output 3D reconstructions of the observed scene structure and texture that demonstrates future applications in autonomous monitoring and surveying.

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ID Code: 53102
Item Type: Conference Paper
Keywords: Computer Vision, UAV, Visual Odometry, Pose Estimation, Structure from Motion
DOI: 10.1007/978-3-642-40686-7_33
ISBN: 978-3-642-40685-0
Subjects: Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Adaptive Agents and Intelligent Robotics (080101)
Divisions: Current > Schools > School of Electrical Engineering & Computer Science
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright 2012 [please consult the author]
Deposited On: 16 Aug 2012 13:44
Last Modified: 29 May 2014 02:17

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