Long-term motion estimation from images - Robotics Institute Carnegie Mellon University

Long-term motion estimation from images

Dennis Strelow and Sanjiv Singh
Conference Paper, Proceedings of 10th International Symposium on Experimental Robotics (ISER '06), pp. 65 - 74, July, 2006

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

@conference{Strelow-2006-9534,
author = {Dennis Strelow and Sanjiv Singh},
title = {Long-term motion estimation from images},
booktitle = {Proceedings of 10th International Symposium on Experimental Robotics (ISER '06)},
year = {2006},
month = {July},
pages = {65 - 74},
}