Feature Motion for Monocular Robot Navigation

Abstract

Monocular vision based robot navigation requires feature tracking for localization. In this paper we present a tracking system using discriminative features as well as less discriminative features. Discriminative features such as SIFT are easily tracked and useful to obtain the initial estimates of the transforms such as affinities and homographies. On the other hand less discriminative features such as Harris corners and manually selected features are not easily tracked in a subsequent frame due to problems in matching. We use SIFT features to obtain the the estimates of the planar homographies representing the motion of the major planar structures in the scene. Planar structure assumption is valid for indoor and architectural scenes. The combination of discriminative and less discriminative feature are tracked using the prediction by these homographies. Then normalized cross correlation matching is used to find the exact matches. This produces robust matching and feature motion can be accurately estimated. We show the performance of our system with real image sequences.