Multi-Sensor Perception and Data Fusion
Conferencia plenaria en SAAEI 2024 por IMS Distinguished Lecturer
Date and Time
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- Date: 04 Jul 2024
- Time: 11:00 AM to 12:00 PM
- All times are (UTC+02:00) Madrid
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- Plaza del Guitarrista Manuel Cano, 2, Ronda,
- Granada, Andalucia
- Spain 18004
- Building: Gran Hotel Luna
- Room Number: Sala plenaria
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- Co-sponsored by SAAEI 2024
- Starts 20 June 2024 12:00 AM
- Ends 04 July 2024 12:00 AM
- All times are (UTC+02:00) Madrid
- No Admission Charge
Speakers
Daniel Watzenig of Graz University of Technology
Multi-Sensor Perception and Data Fusion
Overview of different sensor data fusion taxonomies as well as different ways to model the environment (dynamic object tracking vs. occupancy grid) in the Bayesian framework including uncertainty quantification
• Exploiting potential problems of sensor data fusion, e.g. data association, outlier treatment, anomalies, bias, correlation, or out-of-sequence measurements
• Propagation of uncertainties from object recognition to decision making based on selected examples, e.g. the real-time vehicle pose estimation based on uncertain measurements of different sources (GPS, odometry, lidar) including the discussion of fault detection and localization (sensor drift, breakdown, outliers etc.)
Sensor fusion overcomes the drawbacks of current sensor technology by combining information from many independent sources of limited accuracy and reliability. This makes the system less vulnerable to random and systematic failures of a single component. Multi-source information fusion avoids the perceptual limitations and uncertainties of a single sensor and forms a more comprehensive perception and recognition of the environment including static and dynamic objects. Through sensor fusion we combine readings from different sensors, remove inconsistencies and combine the information into one coherent structure. This kind of processing is a fundamental feature of all animal and human navigation, where multiple information sources such as vision, hearing and balance are combined to determine position and plan a path to a destination. In addition, several readings from the same sensor are combined, making the system less sensitive to noise and anomalous observations. In general, multi-sensor data fusion can achieve an increased classification accuracy of objects, improved state estimation accuracy, improved robustness for instance in adverse weather conditions, an increased availability, and an enlarged field of view. Emerging applications such as autonomous driving systems that are in direct contact and interact with the real world, require reliable and accurate information about their environment in real-time.
Biography:
Daniel Watzenig was born in Austria. He holds a doctorate in electrical engineering and was awarded the venia docendi (adjunct professorship) for electrical measurement science and signal processing from Graz University of Technology, Austria. He is CTO and Head of the Electronics Systems and Software Department at Virtual Vehicle Research Graz. In addition, he was appointed as a Full Professor of Multi-Sensor Perception of Autonomous Systems at the Institute of Computer Graphics and Vision, Faculty of Computer Science and Biomedical Engineering, Graz University of Technology, Austria.
His research interests focus on sense & control of autonomous vehicles, sensor fusion, reinforcement learning and decision making under uncertainty. He is the author or co-author of over 200 peer-reviewed papers, book chapters, patents, and articles. He is the Editor-in-Chief of the SAE Int. Journal on Connected and Automated Vehicles (SAE JCAV). Since 2019 he is invited guest lecturer at Stanford University, USA, teaching multi-sensor perception for autonomous systems (Principles of Robot Autonomy). He is the founder of the Autonomous Racing Graz Team. Since 2024 he is Vice Chair and Member of the Executive Committee of the IEEE Austria Section. He is IEEE Distinguished Lecturer in the field of autonomous vehicles, Board Member of the INSIDE Industry Association (European Initiative on Intelligent Digital Systems) and Member of the Academic Advisory Council of PAVE (global Partners for Automated Vehicle Education). He has been a consultant and appointed expert for military robotics for the Armaments and Defense Technology Agency of the Austrian Armed Forces since 2019.
Email:
Address:Graz University of Technology, , Graz, Spain