Abstract
In this paper, we present a framework for the hybrid omnidirectional and perspective robot vision system. Based on the hybrid imaging geometry, a generalized stereo approach is developed via the construction of virtual cameras. It is then used to rectify the hybrid image pair using the perspective projection model. The proposed method not only simplifies the computation of epipolar geometry for the hybrid imaging system, but also facilitates the stereo matching between the heterogeneous image formation. Experimental results for both the synthetic data and real scene images have demonstrated the feasibility of our approach.
Highlights
In the past few decades, the imaging geometry for perspective cameras has been well studied, first by the photogrammetry and by the computer vision community
Similar to the conventional stereo vision systems, it is important to understand the performance of correspondence matching with respect to various camera parameter settings of the proposed hybrid imaging system; given a fixed translation and orientation between the omnidirectional and perspective cameras, how to achieve better feature detection and matching results by adjusting their focal lengths and image resolutions
While the intrinsic parameters can be directly changed for the perspective camera, those associated with the omnidirectional camera are only accessible in terms of the virtual images
Summary
In the past few decades, the imaging geometry for perspective cameras has been well studied, first by the photogrammetry and by the computer vision community. Multiple view relations between perspective cameras have been established based on projective geometry [1]. Recent research on geometric image formation is gradually moving toward the construction of catadioptric imaging models, which combines lenses and mirrors to increase the field of view [2,3]. Epipolar geometry and camera calibration for various types of catadioptric imaging models are extensively investigated [6,7,8]. An omnidirectional camera is capable of capturing images of extremely large scenes, it suffers from low and non-uniform image resolution
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