Visual localization using an optimal sampling of bags-of-features with entropy applied to repeatable test methods

Abstract

This paper investigates the visual Localization of a mobile platform using a new sampling adaptive bag-of-features patches techniques. The method is specifically developed for the navigation of robots. It is based on the idea of an adaptive dense sampling of images using an optimal multilayer Quadtree decomposition of the image driven by the quantity and homogeneity of the information contained within subpatches. Extracted patches will be of different sizes according to the covered zones in the image. Experimental results carried out on real images in the case of a navigation of a mobile robot using an omnidirectional camera are presented. The method of generating maps will be briefly introduced. Large amount of measures in different cases of noise and occlusion will be presented showing the robustness of the method.

Auto-organized visual perception using distributed camera network

“…It appears clearly that the image is decomposed more coherently, a complete overview of the method can be found in [11].…”

“…Several aspects of the environment can also be extracted from images like walls, doors and vacant spaces [10]. Recent works on bag-of-features [11] representations have become popular, as they introduce geometry free features to characterize local subimage using statistical tools. The aim of this paper is to introduce a geometry-free method that allows camera networks systems to estimate their topology and auto-organize their own activities according to the content of the scene and the task to be achieved.…”