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Paper TuC01.2

Han, Kyuseo (Purdue University), Aeschliman, Chad (Purdue University), Park, Johnny (Purdue University), Kak, Avinash (Purdue University), Kwon, Hyukseong (US Air Force Academy), Pack, Daniel (US Air Force Academy)

UAV Vision: Feature Based Accurate Ground Target Localization through Propagated Initializations and Interframe Homographies

Scheduled for presentation during the Regular Session "Autonomy and Vision for UAVs" (TuC01), Tuesday, May 15, 2012, 14:45−15:00, Meeting Room 1 (Mini-sota)

2012 IEEE International Conference on Robotics and Automation, May 14-18, 2012, RiverCentre, Saint Paul, Minnesota, USA

This information is tentative and subject to change. Compiled on December 13, 2017

Keywords Aerial Robotics

Abstract

Our work presents solutions to two related vexing problems in feature-based localization of ground targets in Unmanned Aerial Vehicle(UAV) images: (i) A good initial guess at the pose estimate that would speed up the convergence to the final pose estimate for each image frame in a video sequence; and (ii) Time-bounded estimation of the position of the ground target. We address both these problems within the framework of the ICP (Iterative Closest Point) algorithm that now has a rich tradition of usage in computer vision and robotics applications. We solve the first of the two problems by frame-to-frame propagation of the computed pose estimates for the purpose of the initializations needed by ICP. The second problem is solved by terminating the iterative estimation process at the expiration of the available time for each image frame. We show that when frame-to-frame homography is factored into the iterative calculations, the accuracy of the position calculated at the time of bailing out of the iterations is nearly always sufficient for the goals of UAV vision.

 

 

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