The Generalized Feature Vector (GFV) : A New Approach for Vision Based Navigation of Outdoor Mobile Robot.

Abstract

This paper studies various methods of feature detection and introduces a new approach termed as Generalized Feature Vector which essentially encapsulates multiple feature parameters for consistent detection. The basic idea of GFV stems out from the fact that if detection relies on a single feature it may lead to multiple false alarms or wrong identification. This error can be greatly reduced to a large extent when a number of features are used instead of one. Though primarily applied to vision sensors GFV has the potential to include data obtained from other types of sensors. Experimental analysis of the proposed method is also included, which shows that the approach is resilient to the extrinsic parametric variations with minimal false alarm rate. This also shows that GFV method has tremendous potential in areas like robot navigation, surveillance, remote sensing and many more.