Hugo Courtois
Papers
2
Total Citations
17
H-Index
2
About
Hugo Courtois is a robotics researcher whose work focuses on sensor fusion and 3D mapping for autonomous systems. His key contributions lie in developing algorithms that combine data from multiple sensors—such as stereo cameras and LiDAR—to create accurate, real-time environmental representations. His 2017 paper, "Fusion of stereo and Lidar data for dense depth map computation" (10 citations), introduced a method for generating dense depth maps critical for obstacle detection and navigation in robotics. More recently, his 2023 work, "NDT RC: Normal Distribution Transform Occupancy 3D Mapping With Recentering" (7 citations), addresses a fundamental limitation in dynamic 3D mapping: the fixed boundaries of occupancy maps that restrict unbounded robot movement. By proposing a recentering algorithm, Courtois enables continuous, memory-efficient mapping for robots operating in large, unbounded environments. His research is particularly impactful for applications in autonomous navigation, where robust depth perception and scalable mapping are essential. Courtois’s work demonstrates a clear trajectory from sensor fusion to advanced mapping solutions, making him a notable contributor to the field of mobile robotics and spatial perception.
Research Focus
Key Achievements
Top Papers
- 1Fusion of stereo and Lidar data for dense depth map computation10 citations · 2017
- 2