Raphael Voges
Papers
6
Total Citations
55
H-Index
4
About
Raphael Voges is a robotics researcher whose work centers on sensor fusion, localization, and state estimation for autonomous systems. His primary contributions lie in developing interval-based methods that account for bounded uncertainty in multi-sensor setups, particularly for visual-inertial and LiDAR systems. Voges is best known for pioneering timestamp offset calibration techniques for IMU-camera systems under interval uncertainty (21 citations), addressing the critical challenge of synchronizing heterogeneous sensor data streams. His work on interval-based visual-LiDAR sensor fusion (12 citations) and visual-inertial LiDAR SLAM (7 citations) has advanced robust localization by propagating sensor errors through set-membership analysis rather than probabilistic assumptions. Voges also contributed to distributed execution of formal specifications on IoT-connected robots (7 citations), bridging the gap between high-level scenario descriptions and real-time robotic control. His research on timestamp offset determination for actuated laser scanners (4 citations) and stereo-visual-LiDAR fusion (4 citations) further demonstrates his systematic approach to handling measurement uncertainty. Through these contributions, Voges has established himself as a key figure in interval-based sensor fusion, offering mathematically rigorous alternatives to traditional Kalman filtering approaches for safety-critical robotic applications.
Research Focus
Key Achievements
Top Papers
- 1
- 2Interval-Based Visual-LiDAR Sensor Fusion12 citations · 2021
- 3Interval-based Visual-Inertial LiDAR SLAM with Anchoring Poses7 citations · 2022
- 4
- 5
- 6Stereo-Visual-LiDAR Sensor Fusion Using Set-Membership Methods4 citations · 2021