Reference : Online learning-based robust visual tracking for autonomous landing of Unmanned Aeria...
Scientific congresses, symposiums and conference proceedings : Paper published in a book
Engineering, computing & technology : Aerospace & aeronautics engineering
Online learning-based robust visual tracking for autonomous landing of Unmanned Aerial Vehicles
Fu, Changhong [Universidad Politecnica de Madrid-UPM-CAR, Computer Vision Group]
Carrio, A. [Universidad Politecnica de Madrid-UPM-CAR, Computer Vision Group]
Olivares Mendez, Miguel Angel mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) >]
Campoy, P. [Universidad Politecnica de Madrid-UPM-CAR, Computer Vision Group]
Unmanned Aircraft Systems (ICUAS), 2014 International Conference on
The 2014 International Conference on Unmanned Aircraft Systems
May 2014
[en] aircraft landing guidance ; autonomous aerial vehicles ; image representation ; mobile robots ; object tracking ; robot vision ; UAV ; appearance change ; arbitrary field ; autolanding task
[en] Autonomous landing is a challenging and important technology for both military and civilian applications of Unmanned Aerial Vehicles (UAVs). In this paper, we present a novel online adaptive visual tracking algorithm for UAVs to land on an arbitrary field (that can be used as the helipad) autonomously at real-time frame rates of more than twenty frames per second. The integration of low-dimensional subspace representation method, online incremental learning approach and hierarchical tracking strategy allows the autolanding task to overcome the problems generated by the challenging situations such as significant appearance change, variant surrounding illumination, partial helipad occlusion, rapid pose variation, onboard mechanical vibration (no video stabilization), low computational capacity and delayed information communication between UAV and Ground Control Station (GCS). The tracking performance of this presented algorithm is evaluated with aerial images from real autolanding flights using manually- labelled ground truth database. The evaluation results show that this new algorithm is highly robust to track the helipad and accurate enough for closing the vision-based control loop.

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