@inproceedings{Warren2010,
abstract = {This paper presents the development of a low- cost sensor platform for use in ground-based vi- sual pose estimation and scene mapping tasks. We seek to develop a technical solution using low-cost vision hardware that allows us to accu- rately estimate robot position for SLAM tasks. We present results from the application of a vi- sion based pose estimation technique to simul- taneously determine camera poses and scene structure. The results are generated from a dataset gathered traversing a local road at the St Lucia Campus of the University of Queens- land. We show the accuracy of the pose esti- mation over a 1.6km trajectory in relation to GPS ground truth.},
address = {Brisbane},
author = {Warren, Michael and McKinnon, D. and He, H. and Upcroft, Ben},
booktitle = {Australasian Conference on Robotics and Automation},
editor = {Wyeth, Gordon and Upcroft, Ben},
file = {:E$\backslash$:/Files/c39881.pdf:pdf},
keywords = {Stereo vision,computer vision,dataset,field robotics,visual odometry},
mendeley-tags = {computer vision,field robotics,visual odometry},
publisher = {Australian Robotics and Automation Association},
title = {{Unaided stereo vision based pose estimation}},
url = {http://eprints.qut.edu.au/39881/},
year = {2010}
}