Papers
2
Total Citations
17
H-Index
2
About
Maxime Derome is a robotics researcher specializing in real-time perception and autonomous navigation, with a focus on passive stereo-vision systems. His work addresses the critical challenge of enabling mobile robots to detect, track, and safely avoid moving obstacles using only visual input. Derome’s most influential contribution, "Real-time mobile object detection using stereo" (2014, 15 citations), introduces a computationally efficient method that leverages dense stereo and optical flow to identify moving objects in dynamic environments—a key capability for field robotics. He extended this work in "Detection, Estimation and Avoidance of Mobile Objects Using Stereo-Vision and Model Predictive Control" (2017, 2 citations), where he proposed a complete perception-to-action pipeline integrating detection, state estimation, and model predictive control for autonomous vehicle navigation. This closed-loop approach demonstrates how stereo vision can drive real-time collision avoidance without relying on expensive sensors like LiDAR. Derome’s research is particularly notable for its emphasis on computational efficiency, making advanced perception algorithms practical for embedded robotic systems. His contributions are foundational for students and engineers working on vision-based autonomous navigation in unstructured, dynamic environments.
Research Focus
Key Achievements
Top Papers
- 1Real-time mobile object detection using stereo15 citations · 2014
- 2