Jane Mulligan's Home Page
The Intelligence in Action Lab
What I do
I work on speed, accuracy and representational issues for
real-time stereo reconstruction for telepresence. I am interested in
the feasibility of telepresence systems as a communications medium for
remote collaboration. I also work on sensing and feature selection for
autonomous robot tasks.
Research
Area:
Stereo vision; Modeling and prediction from depth
sequences; Telepresence; Experimental analysis of robotic tasks;
autonomous navigation; robotic manipulation; integration of
computational vision and manipulation.
Students
Advice to PhD Students.
Recent Publications:
- Wei Xu and Jane Mulligan, Robust relative pose estimation with
integrated cheirality constraint, In
Proc. 19th Intl. Conf on Pattern. Recognition , Dec. 2008.
- Michael J. Procopio, Jane Mulligan, and Greg
Grudic. Learning in Dynamic Environments with Ensemble Selection for
Autonomous Outdoor Robot Navigation. In Proc. Intl. Conf on
Intelligent Robots and Systems (IROS08) , NIce, France, Sept. 2008.
- Michael J. Procopio, Jane Mulligan, and Greg Grudic. Long-term learning using multiple models for outdoor autonomous robot navigation.
In Proc. Intl. Conf on Intelligent Robots and Systems (IROS07), San
Diego, CA, Oct. 2007.
- Michael W. Otte, Scott G. Richardson, Jane Mulligan, and
Gregory Grudic. Local path planning in image space for autonomous
robot navigation in unstructured environments. In Proc. Intl. Conf
on Intelligent Robots and Systems (IROS07), San Diego, CA,
Oct. 2007.
- Greg Grudic, Jane Mulligan, Michael Otte, and Adam
Bates. Online learning of multiple perceptual models for navigation in
unknown terrain. In Proceedings of the 6th International Conference
on Field and Service Robotics, Chamonix, Fr, 2007.
- Michael Procopio, Thomas Strohmann, Adam Bates, Greg Grudic, and
Jane Mulligan. Using binary classifiers to augment stereo
vision for enhanced autonomous robot navigation.Computer Science
CU-CS-1027-07, University of Colorado, Boulder, CO, April
2007.
- Jane Mulligan. Speed versus quality - measuring and optimizing stereo for telepresence. In Laurence
Harris and Michael Jenkin, editors, Computational Vision in Neural and Machine Systems. Cambridge
University Press, 2007.
- G. Grudic and J. Mulligan. Outdoor path labeling using polynomial mahalanobis distance. In Proceedings of Robotics: Science and Systems, Philadelphia, USA, August 2006. [pdf]
- P. Sambhoos, A. Hasan, R. Han, T. Lookabaugh, and
J. Mulligan. "Weeblevideo:
Wide angle field-of-view video sensor networks". In Workshop
on Distributed Smart Cameras (DSC06), 2006, held in conjunction
with ACM SenSys 2006.
- Wei Xu, John Penners, and Jane Mulligan. Recording real worlds for playback in a virtual exercise environment.
Computer Science CU-CS 1013-06, University of Colorado at Boulder, Boulder, CO, 2006. [pdf] .
- Nicole Atzpadin and Jane Mulligan .
Stereo Analysis.
In O. Schreer, P. Kauff, and T. Sikora, editors, 3D
Videocommunication , Chapter 7. Wiley, New York, 2005.
[publisher]
- Jane Mulligan and Greg Grudic.
Topological mapping from image sequences.
In Proceedings of the IEEE Workshop on Learning in Computer
Vision and Pattern Recognition (with CVPR05) , June 2005.
[pdf]
- Gregory Grudic and Jane Mulligan.
Topological mapping with multiple visual manifolds.
In Proceedings of Robotics: Science and Systems, 2005. [pdf]
- Jane Mulligan.
Upper body pose estimation from stereo and hand-face tracking.
In Proceedings of the Second Canadian Conference on Computer and
Robot Vision (CRV 2005) , 2005. [pdf]
- Jane Mulligan, Xenophon Zampoulis, Nikhil Kelshikar, and
Kostas Daniilidis. Stereo-based environment scanning for immersive
telepresence. IEEE Transactions on Circuits and Systems for Video
Technology, pp. 304-320, Vol 14:3, March 2004.
CSCI 5722 Computer Vision (Spring'08)
Campus maps.
Engineering Center Map.
My Curriculum Vitae.
Useful pointers.
Greg Grudic and I organized the NIPS Workshop on
Machine Learning Based Robotics in Unstructured Environments
(2005). and the NIPS 2006 Workshop on Learning Applied
to Ground Robots: Sensing and Locomotion.