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KALMANSAC: Robust filtering by consensus

Abstract:

We propose an algorithm to perform causal inference of the state of a dynamical model when the measurements are corrupted by outliers. While the optimal (maximum-likelihood) solution has doubly exponential complexity due to the combinatorial explosion of possible choices of inliers, we exploit the structure of the problem to design a sampling-based algorithm that has constant complexity. We derive our algorithm from the equations of the optimal filter, which makes our approximation explicit. ...

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Publisher copy:
10.1109/ICCV.2005.130

Authors


Vedaldi, A More by this author
Journal:
Proceedings of the IEEE International Conference on Computer Vision
Volume:
I
Pages:
633-640
Publication date:
2005
DOI:
URN:
uuid:492e2d64-ffb2-4aba-aace-ddaaaaf0a932
Source identifiers:
284940
Local pid:
pubs:284940
Language:
English

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