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Long-term motion estimation from images

Dennis Strelow and Sanjiv Singh
Conference Paper, Carnegie Mellon University, In Proceedings, International Symposium on Experimental Robotics, July, 2006

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Abstract

Cameras are promising sensors for estimating the motion of autonomous vehicles without GPS and for automatic scene modeling. Furthermore, a wide variety of shape-from-motion algorithms exist for simultaneously estimating the camera’s six degree of freedom motion and the three-dimension structure of the scene, without prior assumptions about the camera’s motion or an existing map of the scene. However, existing shape-from-motion algorithms do not address the problem of accumulated long-term drift in the estimated motion and scene structure, which is critical in autonomous vehicle applications. The paper introduces a proof of concept system that exploits a new tracker, the variable state dimension filter (VSDF), and SIFT keypoints to recognize previously visited locations and limit drift in long-term camera motion estimates. The performance of this system on an extended image sequence is described.

BibTeX Reference
@conference{Strelow-2006-9534,
title = {Long-term motion estimation from images},
author = {Dennis Strelow and Sanjiv Singh},
booktitle = {In Proceedings, International Symposium on Experimental Robotics},
school = {Robotics Institute , Carnegie Mellon University},
month = {July},
year = {2006},
address = {Pittsburgh, PA},
}
2017-09-13T10:42:38+00:00