Abstract

Simultaneous Localization and Mapping, SLAM, for mobile robots using a single camera, has attracted several researchers in the recent years. In this paper, we study the effect of feature point geometrical composition on the associated localization errors. The study will help to design an efficient feature management strategy that can reach high accuracy using fewer features. The basic idea is inspired from camera calibration literature which requires calibration target points to have significant perspective effect to derive accurate camera parameters. When the scene have significant perspective effect, it is expected that this will reduce the errors since it implicitly comply with the utilized perspective projection model. Experiments were done to explore the effect of scene features composition on the localization errors using the state of the art visual Mono SLAM algorithm.