24 January 2011 Spectrally queued feature selection for robotic visual odometery
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Abstract
Over the last two decades, research in Unmanned Vehicles (UV) has rapidly progressed and become more influenced by the field of biological sciences. Researchers have been investigating mechanical aspects of varying species to improve UV air and ground intrinsic mobility, they have been exploring the computational aspects of the brain for the development of pattern recognition and decision algorithms and they have been exploring perception capabilities of numerous animals and insects. This paper describes a 3 month exploratory applied research effort performed at the US ARMY Research, Development and Engineering Command's (RDECOM) Tank Automotive Research, Development and Engineering Center (TARDEC) in the area of biologically inspired spectrally augmented feature selection for robotic visual odometry. The motivation for this applied research was to develop a feasibility analysis on multi-spectrally queued feature selection, with improved temporal stability, for the purposes of visual odometry. The intended application is future semi-autonomous Unmanned Ground Vehicle (UGV) control as the richness of data sets required to enable human like behavior in these systems has yet to be defined.
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David M. Pirozzo, Philip A. Frederick, Shawn Hunt, Bernard Theisen, Mike Del Rose, "Spectrally queued feature selection for robotic visual odometery", Proc. SPIE 7878, Intelligent Robots and Computer Vision XXVIII: Algorithms and Techniques, 78780Q (24 January 2011); doi: 10.1117/12.876679; https://doi.org/10.1117/12.876679
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