Online Variational Bayesian Motion Averaging
| Autor: | Guillaume Bourmaud |
|---|---|
| Přispěvatelé: | Bourmaud, Guillaume |
| Jazyk: | angličtina |
| Rok vydání: | 2016 |
| Předmět: |
variational Bayes
0209 industrial biotechnology Mean squared error Scale (ratio) Computer science visual SLAM Posterior probability Bayesian probability ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Motion (geometry) 02 engineering and technology Filter (signal processing) pose-graph filtering Lie group Loop (topology) motion averaging 020901 industrial engineering & automation [INFO.INFO-CV] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV] 0202 electrical engineering electronic engineering information engineering large scale 020201 artificial intelligence & image processing relative parametrization Parametrization Algorithm |
| Zdroj: | Computer Vision – ECCV 2016 ISBN: 9783319464831 ECCV (8) |
| Popis: | In this paper, we propose a novel algorithm dedicated to online motion averaging for large scale problems. To this end, we design a filter that continuously approximates the posterior distribution of the estimated transformations. In order to deal with large scale problems, we associate a variational Bayesian approachwith a relative parametrization of the absolute transformations. Such an association allows our algorithm to simultaneously possess two features that are essential for an algorithm dedicated to large scale online motion averaging: (1) a low computational time, (2) the ability to detect wrong loop closure measurements. We extensively demonstrate on several applications (binocular SLAM, monocular SLAM and video mosaicking) that our approach not only exhibits a low computational time and detects wrong loop closures but also significantly outperforms the state of the art algorithm in terms of RMSE. |
| Databáze: | OpenAIRE |
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