This paper presents a general method for combining stereo surfaces using a Kalman filter. A measure of error in surface representation is suggested, and the work shows how a set of surfaces may be combined to give a single surface which minimises this measure. The analysis shows how a stochastic surface may be generated using stereo, and how errors in surface-to-surface registration may be modeled. The cases of multiple, mutually-occluding surfaces and unknown three-dimensional camera motion are considered. Performance is analysed using semi-artificial data. The results are important to multi-sensor fusion and automatic model generation.
KeywordsControl Point Registration Error Stereo Pair Positional Uncertainty Inverse Depth
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