Papers
7
Total Citations
573
H-Index
6
About
Christopher Mei is a leading researcher in robotics and computer vision, whose work has fundamentally advanced the fields of omnidirectional camera calibration and visual place recognition. His most influential contribution, the 2007 paper "Single View Point Omnidirectional Camera Calibration from Planar Grids," has garnered over 429 citations and provides a flexible, widely-adopted method for calibrating these increasingly important sensors in robotics. Mei's research addresses core challenges in autonomous navigation, including robust localization and mapping. He pioneered the use of covisibility graphs for probabilistic place recognition, a concept that has shaped how robots build and recall location models. His work on homography-based tracking for central catadioptric cameras and laser-augmented omnidirectional vision for SLAM demonstrates his commitment to creating practical, large-scale autonomous systems. Notably, his 2010 study "Planes, trains and automobiles" tackled the ambitious goal of enabling autonomous navigation in complex urban environments, processing over 181GB of real-world sensory data. Through these contributions, Mei has established himself as a key figure in developing the perception and mapping capabilities that underpin modern robotic autonomy.
Research Focus
Key Achievements
Top Papers
- 1Single View Point Omnidirectional Camera Calibration from Planar Grids429 citations · 2007
- 2Probabilistic place recognition with covisibility maps40 citations · 2013
- 3Location graphs for visual place recognition30 citations · 2015
- 4Homography-based Tracking for Central Catadioptric Cameras27 citations · 2006
- 5Laser-augmented omnidirectional vision for 3D localisation and mapping27 citations · 2007
- 6Planes, trains and automobiles — autonomy for the modern robot17 citations · 2010
- 7Robust and accurate pose estimation for vision-based localisation3 citations · 2012