Authors:
Zhaozheng Hu
1
and
Takashi Matsuyama
2
Affiliations:
1
Kyoto University and Dalian Maritime University, Japan
;
2
Kyoto University, Japan
Keyword(s):
Vision-based Localization, Bayesian Perspective-plane (BPP), Plane Normal, Maximum Likelihood.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Computer Vision, Visualization and Computer Graphics
;
Features Extraction
;
Geometry and Modeling
;
Image and Video Analysis
;
Image-Based Modeling
;
Motion, Tracking and Stereo Vision
;
Pattern Recognition
;
Software Engineering
;
Tracking and Visual Navigation
Abstract:
The "perspective-plane" problem proposed in this paper is similar to the "perspective-n-point (PnP)" or "perspective-n-line (PnL)" problems, yet with broader applications and potentials, since planar scenes are more widely available than control points or lines in practice. We address this problem in the Bayesian framework and propose the "Bayesian perspective-plane (BPP)" algorithm, which can deal with more generalized constraints rather than type-specific ones to determine the plane for localization. Computation of the plane normal is formulated as a maximum likelihood problem, and is solved by using the Maximum Likelihood Searching Model (MLS-M). Two searching modes of 2D and 1D are presented. With the computed normal, the plane distance and the position of the object or camera can be computed readily. The BPP algorithm has been tested with real image data by using different types of scene constraints. The 2D and 1D searching modes were illustrated for plane normal computation. Th
e results demonstrate that the algorithm is accurate and generalized for object localization.
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