Date of Original Version

7-2006

Type

Conference Proceeding

Abstract or Description

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.

DOI

10.1007/978-3-540-77457-0_7

Included in

Robotics Commons

Share

COinS
 

Published In

Springer Tracts in Advanced Robotics , 39, 65-74.