Papers
4
Total Citations
73
H-Index
3
About
Maxime Meilland is a leading researcher in mobile robotics and autonomous navigation, with a focus on vision-based perception and human-robot interaction. His pioneering work on spherical robot-centered representations for urban navigation introduced a novel method that combines spherical images with depth information and saliency maps, enabling robust visual localization in complex city environments—a foundational contribution cited over 45 times. Meilland also advanced assistive robotics through his development of a brain-computer interface (BCI) for controlling a humanoid robot using steady-state visually evoked potentials (SSVEP), allowing users to navigate and interact with their surroundings via neural signals alone. His hybrid laser/omnidirectional sensor approach for appearance-based SLAM achieved efficient 3D mapping by fusing laser range data with omnidirectional vision, earning recognition for its practical impact on autonomous robot positioning. More recently, Meilland has explored dense map building from spherical RGB-D images, contributing to the evolution of visual odometry and pose graph construction. With a career spanning foundational sensing methods to cutting-edge BCI applications, Meilland’s work continues to shape how robots perceive, navigate, and assist in real-world environments.
Research Focus
Key Achievements
Top Papers
- 1A spherical robot-centered representation for urban navigation45 citations · 2010
- 2Navigation assistance for a BCI-controlled humanoid robot13 citations · 2014
- 3Appearance-based SLAM relying on a hybrid laser/omnidirectional sensor12 citations · 2010
- 4A Dense Map Building Approach from Spherical RGBD Images3 citations · 2014