Authors:
Mohamed Heshmat
and
Mohamed Abdellatif
Affiliation:
Egypt-Japan University of Science and Technology (EJUST), Egypt
Keyword(s):
Monocular Visual SLAM, Feature Composition, Feature Selection Criteria.
Related
Ontology
Subjects/Areas/Topics:
Active and Robot Vision
;
Applications
;
Computer Vision, Visualization and Computer Graphics
;
Geometry and Modeling
;
Image-Based Modeling
;
Motion, Tracking and Stereo Vision
;
Pattern Recognition
;
Robotics
;
Software Engineering
;
Tracking and Visual Navigation
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.