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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.

Notes

Associated Center(s) / Consortia: Field Robotics Center

Number of pages: 10

Text Reference

Dennis Strelow and Sanjiv Singh, "Long-term motion estimation from images," In Proceedings, International Symposium on Experimental Robotics, July, 2006.

BibTeX Reference

@inproceedings{Strelow_2006_5537,
   author = "Dennis Strelow and Sanjiv Singh",
   title = "Long-term motion estimation from images",
   booktitle = "In Proceedings, International Symposium on Experimental Robotics",
   month = "July",
   year = "2006",
}

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