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Erschienen in:
2010 | OriginalPaper | Buchkapitel
Range Image Registration Using Particle Filter and Competitive Associative Nets
verfasst von : Shuichi Kurogi, Tomokazu Nagi, Takeshi Nishida
Erschienen in: Neural Information Processing. Models and Applications
Verlag: Springer Berlin Heidelberg
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This paper presents a method using a particle filter (PF) and competitive associative nets (CAN2s) for range image registration to fuse 3D surfaces on range images taken from around an object by the laser range finder (LRF). The method uses the CAN2 for learning to provide a piecewise linear approximation of the LRF data involving various noise, and obtaining a coarse but fast pair-wise registration. The PF is used for reducing the cumulative error of the consecutive pair-wise registration. The effectiveness is shown by using the real LRF data of a rectangular box.