Authors:
Hsiang-Jen Chien
and
Reinhard Klette
Affiliation:
Auckland University of Technology, New Zealand
Keyword(s):
Visual Odometry, Camera Motion Recovery, Perspective-n-points Problem, Nonlinear Energy Minimisation.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Computer Vision, Visualization and Computer Graphics
;
Motion, Tracking and Stereo Vision
;
Pattern Recognition
;
Robotics
;
Software Engineering
;
Tracking and Visual Navigation
Abstract:
For two decades, ego-motion estimation is an actively developing topic in computer vision and robotics. The
principle of existing motion estimation techniques relies on the minimisation of an energy function based on
re-projection errors. In this paper we augment such an energy function by introducing an epipolar-geometry-derived
regularisation term. The experiments prove that, by taking soft constraints into account, a more reliable
motion estimation is achieved. It also shows that the implementation presented in this paper is able to achieve
a remarkable accuracy comparative to the stereo vision approaches, with an overall drift maintained under 2%
over hundreds of metres.