Indoor autonomous navigation using visual memory and pattern tracking

Abstract

The paper deals with autonomous environment mapping, localisation and navigation using exclusively monocular vision and multiple 2D pattern tracking. The environment map is a mosaic of 2D patterns detected on the ceiling plane and used as natural landmarks. The robot is able to reproduce learned trajectories defined by key images representing the visual memory. The pattern tracker is based on particle filetring. It uses both image contours and gray scale level variations to track efficiently 2D patterns on cluttered background. An original observation model used for filter state updating is presented.