Andreas Masselli
Papers
10
Total Citations
141
H-Index
8
About
Andreas Masselli is a leading researcher in the field of autonomous robotics, with a primary focus on vision-based localization, terrain classification, and real-time object detection for unmanned aerial vehicles (UAVs) and mobile robots. His most influential work introduces a novel geometric approach to the classic perspective-three-point (P3P) problem, offering a faster and simpler solution for determining camera position and orientation—a foundational contribution that has garnered 25 citations. Masselli has also pioneered methods for visual terrain classification using SURF features on flying robots, enabling quadrocopters to autonomously interpret ground surfaces from aerial imagery (23 citations). His hybrid approach to outdoor robot localization, which combines global and local image features to overcome challenges like changing illumination, has been cited 21 times. Beyond these core contributions, Masselli has developed robust systems for real-time number sign detection, face detection using geometric constraints and depth-based skin segmentation, and leader-following between quadrotors using onboard vision. His work consistently emphasizes practical, computationally efficient solutions for resource-constrained robotic platforms, making significant strides in enabling autonomous navigation and interaction in complex outdoor environments.
Research Focus
Key Achievements
Top Papers
- 1
- 2Visual terrain classification by flying robots23 citations · 2012
- 3
- 4
- 5Fast Outdoor Robot Localization Using Integral Invariants14 citations · 2019
- 6Robust Real-Time Number Sign Detection on a Mobile Outdoor Robot10 citations · 2011
- 7
- 8Following a quadrotor with another quadrotor using onboard vision9 citations · 2013
- 9
- 10Robust real-time detection of multiple balls on a mobile robot3 citations · 2013