In the context of mobile robotics, my research focuses on scene understanding through embedded multi-modal perception systems. This complex problem is crucial for high-impact societal applications, such as Intelligent Transportation Systems, particularly in Advanced Driver Assistance Systems (ADAS) and Autonomous Vehicles.
Key Research Areas
Simultaneous Localization and Mapping (SLAM)
- Multimodal loop-closure fusion
- HOOFR SLAM Vehicle Localization
- Machine learning and AI-based algorithms
- Hardware/software co-design
Context-Aware Multi-Modal Perception
- Calibration-free data association
Computer Vision
- Multiple-view geometry
- Scene motion reconstruction
Filtering and Multiple Target Tracking
- Bayesian approaches
- Multi-sensor data fusion
- Data association
Ongoing Works for industrial applications academic studies such as
- Embedded rail defect detection system
- Trajectory prediction systems for autonomous vehicles
- Rider behavior characterization using gaze analysis