Date: 30 July 2008 Time: 12:00-14:00
Location: "Stelios Orphanoudakis" Seminar Room, FORTH, Heraklion, Crete
Host: Prof. C. Stephanidis
We present an odometry approach relying on monocular panoramic video over several kilometers in urban streets with midday traffic. While current approaches rely on pose updates from reconstructed proximal landmarks, we introduce a bio-inspired approach where distal landmarks provide robust estimates of absolute orientation obtained from 2D-2D correspondences. Our approach is frame incremental and does not contain any iterative or batch steps.
Mapping requires solution of the loop closing problem. Loop closing as well as arbitrary post-mapping localization are place recognition capabilities. Starting from a pure appearance based approach for place recognition, we investigate geometric and structural methods for matching images based on location. Results are shown in km-long trajectories in mid-day Philadelphia traffic.
Kostas Daniilidis is the director of the interdisciplinary GRASP laboratory and Associate Professor of Computer and Information Science at the University of Pennsylvania.
He obtained his undergraduate degree in Electrical Engineering from the National Technical University of Athens, 1986, and his PhD in Computer Science from the University of Karlsruhe, 1992, under the supervision of Hans-Hellmut Nagel. His research interests are in space and motion perception with machines, with applications on visual navigation, localization, omnidirectional vision, 3D object recognition, and immersive environments.
He was Associate Editor of IEEE Transactions on Pattern Analysis and Machine Intelligence. In 2000, he founded the series of IEEE Workshops on Omnidirectional Vision. In 2006, he co-chaired with Pollefeys the Third Symposium on 3D Data Processing, Visualization, and Transmission. His work on stereo and tele-immersion was featured in Scientific American, April 2001. He appeared in the Discovery Channel feature "Debunked" with his work on archaeological 3D scanning in Costa Rica.