Contactg.dubbelman@ tue.nl +31 40 247 2386 Flux 5.092
Gijs Dubbelman is an Assistant Professor with the Video Coding and Architecture (VCA) group at Eindhoven University of Technology (TU/e)/ Here, he heads the Mobile Perception Systems (MPS) research cluster, which focuses on signal processing technologies that allow mobile sensor platforms to perceive the world around them. His areas of expertise include computer vision and multiple-view geometry and robotics and he has been working on topics such as large-scale visual-odometry, simultaneous localization and mapping, bundle adjustment and on-line camera re-calibration. Gijs also has a background in computer graphics, software engineering and project management.
Key research areas are computer vision, pattern recognition, robotics, and sensor fusion. Important application domain of MPS are automotive and transportation. Gijs’ line of research focuses on 3-D computer vision systems for autonomous robots and vehicles. He has designed and developed state-of-the-art computer vision algorithms for obstacle detection, ego-motion estimation, and simultaneous localization and mapping. He contributed the COP-SLAM algorithm for real-time embedded visual-SLAM to the scientific open-source project OpenSLAM.
Gijs Dubbelman obtained his BSc in Information and Communication Technology and his MSc Degree cum laude in Artificial intelligence from the University of Amsterdam. In 2011 he obtained his PhD from the same university on the topic of intrinsic statistical techniques for robust pose estimation. He performed his PhD research on robust estimation of motion parameters from image data using intrinsic statistics in close cooperation with Intelligent Imaging department of the national organization of applied scientific research of the Netherlands (TNO), which funded the research. In 2011 and 2012 Gijs was a member of the internationally renowned Field Robotics Center of Carnegie Mellon's Robotics Institute. He worked in close collaboration with the National Robotics Engineering Center (NREC) and with CMU's Qatar campus.
Empirical Generalization Study: Unsupervised Domain Adaptation vs. Domain Generalization Methods for Semantic Segmentation in the Wild(2023)
Intra-Batch Supervision for Panoptic Segmentation on High-Resolution Images(2023)
Correction toMachine Learning (2023)
Continual Pedestrian Trajectory Learning With Social Generative ReplayIEEE Robotics and Automation Letters (2023)
Learning to Predict Collision Risk from Simulated Video Data(2022)
Current Educational Activities
No ancillary activities