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A Badly Calibrated Camera in Ego-Motion Estimation, Propagation of Uncertainty

Tomas Svoboda 1 Peter Sturm 2 
2 MOVI - Modeling, localization, recognition and interpretation in computer vision
GRAVIR - IMAG - Laboratoire d'informatique GRAphique, VIsion et Robotique de Grenoble, Inria Grenoble - Rhône-Alpes, CNRS - Centre National de la Recherche Scientifique : FR71
Abstract : This paper deals with the ego-motion estimation (motion of the camera) from two views. When we want to estimate the ego-motion we have to find correspondences and we need a calibrated camera. In this paper we solve the problem how to propagate known camera calibration errors into the uncertainty of the motion parameters. We present a linear estimate of the uncertainty of the motion parameters based on the uncertainty in the calibration parameters. We show that the linear estimate of the motion parameters uncertainty is very stable and useful up to 20 % noise in camera calibration even with a noise in correspondences.
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Submitted on : Tuesday, May 3, 2011 - 9:15:53 AM
Last modification on : Thursday, April 28, 2022 - 12:34:02 AM
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Tomas Svoboda, Peter Sturm. A Badly Calibrated Camera in Ego-Motion Estimation, Propagation of Uncertainty. 7th International Conference on Computer Analysis of Images and Patterns (CAIP '97), Sep 1997, Kiel, Germany. pp.183--190, ⟨10.1007/3-540-63460-6_116⟩. ⟨inria-00590083⟩



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