Adam Selyem
Papers
1
Total Citations
12
H-Index
1
About
Adam Selyem is a researcher at the forefront of autonomous systems and sensor fusion, with a focus on integrating multi-modal perception for real-world automation. His most cited work, "Hybrid 3D ranging and velocity tracking system combining multi-view cameras and simple LiDAR" (2019, 12 citations), addresses a critical challenge in robotics and driverless vehicles: the need for accurate, efficient position, distance, and velocity sensing. Selyem’s key contribution lies in demonstrating how combining low-cost LiDAR with multi-view camera systems can achieve robust 3D tracking without relying on expensive, high-end sensors. This hybrid approach balances cost and performance, making advanced perception more accessible for practical automation. By tackling the fundamental trade-off between sensor complexity and real-time accuracy, Selyem’s work has influenced the design of scalable perception pipelines. His research continues to push the boundaries of how autonomous systems interpret dynamic environments, offering a pragmatic path toward safer and more reliable automated decision-making.
Research Focus
Key Achievements
Top Papers
- 1