Papers
1
Total Citations
4
H-Index
1
About
Bastien Morgue is a researcher at the intersection of robotics, computer vision, and multi-modal perception. His work focuses on enabling robots to robustly track and interact with multiple people in dynamic environments by fusing audio and visual data. Morgue’s key contribution is the development of variational fusion techniques that integrate noisy, asynchronous sensory streams—such as microphone arrays and cameras—to improve tracking accuracy and reliability in real-world settings. His most-cited paper, "Audio-Visual Variational Fusion for Multi-Person Tracking with Robots" (2019, 4 citations), introduces a probabilistic framework that leverages uncertainty to combine modalities, addressing challenges like occlusions and background noise. This work, presented at an international venue, lays the groundwork for more natural human-robot collaboration. While his citation count is modest, Morgue’s research is notable for its methodological rigor and practical relevance to service robotics and autonomous systems. His contributions are particularly valuable for students and researchers interested in sensor fusion, probabilistic robotics, and human-aware navigation.
Research Focus
Key Achievements
Top Papers
- 1Audio-Visual Variational Fusion for Multi-Person Tracking with Robots4 citations · 2019