By Topic

3D pose estimation of vehicles using a stereo camera

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

5 Author(s)
Bjorn Barrois ; Daimler AG, Group Research, Environment Perception, P.O. Box 2360, D-89013 Ulm, Germany ; Stela Hristova ; Christian Wohler ; Franz Kummert
more authors

This study introduces an approach to three-dimensional vehicle pose estimation using a stereo camera system. After computation of stereo and optical flow on the investigated scene, a four-dimensional clustering approach separates the static from the moving objects in the scene. The iterative closest point algorithm (ICP) estimates the vehicle pose using a cuboid as a weak vehicle model. In contrast to classical ICP optimisation a polar distance metric is used which especially takes into account the error distribution of the stereo measurement process. The tracking approach is based on tracking-by-detection such that no temporal filtering is used. The method is evaluated on seven different real-world sequences, where different stereo algorithms, baseline distances, distance metrics, and optimisation algorithms are examined. The results show that the proposed polar distance metric yields a higher accuracy for yaw angle estimation of vehicles than the common Euclidean distance metric, especially when using pixel-accurate stereo points.

Published in:

Intelligent Vehicles Symposium, 2009 IEEE

Date of Conference:

3-5 June 2009