Papers
7
Total Citations
576
H-Index
6
About
Maxime Lhuillier is a prominent researcher specializing in computer vision, mobile robotics, and autonomous navigation, with a particular focus on monocular vision systems. His work has made significant contributions to the challenge of enabling robots to understand and navigate complex real-world environments using only a single camera as input — a constraint that demands sophisticated algorithmic solutions. Lhuillier's most influential contribution, "Monocular Vision for Mobile Robot Localization and Autonomous Navigation" (2007, 324 citations), established a foundational framework in which robots learn a path under human guidance before navigating it autonomously using 3D environmental maps reconstructed from video sequences. This teach-and-repeat paradigm runs through much of his research portfolio. His earlier works, including outdoor navigation and urban localization studies, demonstrated that monocular vision could rival differential GPS sensors in positioning accuracy — a striking result that captured the attention of the robotics community. Lhuillier also advanced Simultaneous Localization and Mapping (SLAM) methodologies and incremental bundle adjustment techniques for 3D reconstruction of complex scenes. Together, these contributions have earned him over 570 citations, cementing his reputation as a key figure in vision-based robotics. His research remains highly relevant to modern autonomous vehicle and drone navigation systems.
Research Focus
Key Achievements
Top Papers
- 1Monocular Vision for Mobile Robot Localization and Autonomous Navigation324 citations · 2007
- 2Outdoor autonomous navigation using monocular vision90 citations · 2005
- 3
- 4Monocular Vision Based SLAM for Mobile Robots46 citations · 2006
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- 7Monocular vision based SLAM for mobile robots5 citations · 2006