Papers
2
Total Citations
20
H-Index
2
About
Serge Olympieff is a researcher advancing the state of the art in real-time 3D perception for robotics. His work focuses on dense RGB-D SLAM and efficient 3D mapping, tackling the critical challenge of enabling robots to understand and interact with dynamic environments using limited computational resources. Olympieff’s most notable contribution is the development of a speed and memory-efficient dense RGB-D SLAM system for dynamic scenes, which has garnered 17 citations. This work addresses a key bottleneck in robotics by proposing a method that maintains high-quality 3D localization and mapping without requiring the heavy, costly hardware typical of surfel-based approaches. Earlier, he introduced a novel lightweight 3D representation called “supersurfels,” designed for fast, coarse, yet relevant mapping of static environments. This approach prioritizes computational efficiency and memory conservation, making it suitable for embedded and mobile platforms. Through these contributions, Olympieff is helping to democratize advanced robotic perception, paving the way for more accessible and responsive autonomous systems.
Research Focus
Key Achievements
Top Papers
- 1Speed and Memory Efficient Dense RGB-D SLAM in Dynamic Scenes17 citations · 2020
- 2