Visual Odometry for Tracked Vehicles

Download: Paper (PDF), Video (mpeg 5.4MB).

“Visual Odometry for Tracked Vehicles” by C. Dornhege and A. Kleiner. In Proc. of the IEEE Int. Workshop on Safety, Security and Rescue Robotics (SSRR), (Gaithersburg, Maryland, USA), 2006.

Abstract

Localization and mapping on autonomous robots typically requires a good pose estimate, which is hard to acquire if the vehicle is tracked. In this paper we describe a solution to the pose estimation problem by utilizing a consumer-quality camera and an Inertial Measurement Unit (IMU). The basic idea is to continuously track salient features with the KLT feature tracker over multiple images taken by the camera and to extract from the tracked features image vectors resulting from the robot's motion. Each image vector is taken for a voting that best explains the robot's motion. Image vectors vote according to a previously trained tile coding classificator that assigns to each possible image vector a translation probability. Our results show that the proposed single camera solution leads to sufficiently accurate pose estimates of the tracked vehicle.

Download: Paper (PDF), Video (mpeg 5.4MB).

BibTeX entry:

@inproceedings{dornhege_et_al_06,
   author = {C. Dornhege and A. Kleiner},
   title = {Visual Odometry for Tracked Vehicles},
   booktitle = {Proc. of the {IEEE} Int. Workshop on Safety, Security and
	Rescue Robotics {(SSRR)}},
   address = {Gaithersburg, Maryland, USA},
   year = {2006}
}

(This webpage was created with bibtex2web.)

Back to Publications.