جست‌وجوی استاد و پژوهشگر

Georg von Wichertمشاهده پروفایل

پژوهشگر

• Cognitive Systems
• Computer Vision
• Robotics
• +۳ مورد دیگر

Georg von Wichert is a Rudolf Diesel Industry Fellow at the Technical University of Munich (TUM) since 2009, affiliated with Siemens Corporate Technology. He holds a Diploma (M.Sc.) in electrical and control engineering from Darmstadt University of Technology (1992) and a Ph.D. in electrical engineering (1998). His research focuses on cognitive systems, including computer vision, robotics, and sensor fusion, with applications in autonomous systems and human-robot interaction. Affiliations: Siemens AG (Corporate Technology) & TUM Institute for Advanced Study (Focus Group: Cognitive Technology) Education: Darmstadt University of Technology (Diploma 1992, Ph.D. 1998) His work spans probabilistic modeling for multi-agent systems, semantic scene understanding, and robotic navigation. Notable contributions include table-top scene analysis, semantic indoor mapping, and robotic systems for everyday environments. He has led national and European research projects and contributed to initiatives like the Robotic Bar demonstration of human-robot collaboration. Scientific Awards: Rudolf Diesel Industry Fellowship (2009). Research Highlights: Developed algorithms for 6-DOF pose estimation, robotic exploration in dynamic environments, and context-aware robotic assistants. His publications address challenges in simultaneous segmentation, object recognition, and 3D localization using stereo vision.

Tobias Fischerمشاهده پروفایل

مدرس ارشد

• Robotics
• Computer Vision
• Computational Cognition
• +۴ مورد دیگر

Dr. Tobias Fischer is a Senior Lecturer at Queensland University of Technology (QUT) in the School of Electrical Engineering & Robotics, Faculty of Engineering. He is an ARC DECRA Fellow and leads research in robotics, computer vision, and computational cognition. His work focuses on enabling robots to interact with humans through perception systems inspired by animal visual systems. He holds a PhD from Imperial College London and has held postdoctoral roles at Imperial College's Personal Robotics Lab. Education: PhD (Imperial College London, 2019), MSc (University of Edinburgh, 2014), BSc (Ilmenau University of Technology, Germany, 2013). Scholarships include the German National Academic Foundation and DECRA Fellowship. Research interests span bio-inspired neural networks, event-based vision, spiking neural networks, and neuromorphic computing. He has led projects funded by Intel, Amazon, Samsung, EU, and Australian Research Council, including $462,000 DECRA grant for adaptive robot positioning. Key awards include the 2023 IEEE Outstanding Paper Award and Queen Mary UK Best PhD in Robotics. Teaching includes units like EGB339 Introduction to Robotics and EGB439 Advanced Robotics. Supervision involves PhD students in place recognition and underwater imagery. His lab, QUT Centre for Robotics, focuses on long-term localization and bio-inspired autonomy.

Niko Suenderhaufمشاهده پروفایل

استاد

• Robotic Vision
• Computer Vision
• Machine Learning
• +۷ مورد دیگر

Professor Niko Suenderhauf is a leading researcher in robotic vision and AI at Queensland University of Technology (QUT). He focuses on enabling robots to understand and interact with their environment through advancements in SLAM, scene understanding, and semantic mapping. His work bridges robotics, computer vision, and machine learning, aiming to create reliable, relatable, and sustainable robotic systems that collaborate with humans to address societal challenges like climate change and aging populations. His key achievements include pioneering graph-based SLAM methods integrated into the ROS ecosystem, developing object-based semantic SLAM systems, and demonstrating the efficacy of deep learning for place recognition. Collaborations with institutions like DeepMind and Amazon reflect his interdisciplinary approach. Awards include recognition for groundbreaking SLAM research and highly cited publications. Research interests emphasize open-world navigation, privacy-preserving vision, and embodied AI, with a focus on practical applications in construction, environmental monitoring, and domestic robotics. His work on uncertainty quantification and robust perception ensures systems operate safely in unpredictable environments. Professor Suenderhauf actively engages in education, mentoring students across undergraduate and postgraduate levels, and emphasizes the societal impact of technology through collaborations with neuroscience, law, and design experts.

Olov Anderssonمشاهده پروفایل

استادیار

• Autonomous Systems
• Robotics
• Artificial Intelligence
• +۵ مورد دیگر

Olov Andersson is an Assistant Professor and WASP Fellow in AI for Autonomous Systems at KTH Royal Institute of Technology, leading the Division of Robotics, Perception and Learning. His research focuses on Embodied AI for autonomous robots and vehicles, combining advancements in Vision-Language Models (VLM), Large Language Models (LLM), and real-world navigation challenges. Key projects include the DARPA SubT Challenge-winning team CERBERUS and the EU H2020 Heron project for robotic road repair. He supervises multiple PhD students and postdocs, including Timon Homberger, Finn Lukas Busch, and Jesper Eriksson. Research interests emphasize full-stack autonomy in dynamic environments, including planning, mapping, and navigation. Notable contributions include the OneMap real-time open-vocabulary mapping system and self-supervised scene flow methods like Seflow. He has been recognized for technical leadership in autonomous systems through awards like the WASP Fellowship. Professional activities include co-chairing the 2024 IROS workshop on robot perception in dynamic environments and advising the Swedish Prime Minister’s AI initiative. Teaching roles span multiple graduate courses in machine learning, robotics, and systems engineering at KTH.

Pascual Campoy Cerveraمشاهده پروفایل

استاد

• Robotics
• Computer Vision
• Unmanned Aerial Vehicles (UAVs)
• +۶ مورد دیگر

Pascual Campoy Cervera is a Full Professor at the Universidad Politécnica de Madrid (UPM) and holds visiting professor positions at Delft University of Technology, Tongji University, and Queensland University of Technology. His work focuses on Control Systems , Machine Learning , and Computer Vision for Unmanned Aerial Vehicles (UAVs) . As Principal Investigator of the Computer Vision and Aerial Robotics group at UPM's Center for Automation and Robotics (CAR), he has led over 40 R&D projects with European, national, and industrial funding. Current affiliations: UPM, TU Delft, CAR-UPM Research themes: UAV autonomy, swarm robotics, embedded vision systems His research integrates cutting-edge technologies in image processing, control theory, and artificial intelligence to enhance UAV capabilities in unstructured environments. Recent projects include: Autonomous firefighting systems High-speed drone racing frameworks Swarm-based solar farm inspection Thrust vectoring for heavy UAVs Notable scientific awards include multiple international prizes at UAV competitions (IMAV12–17). His team has developed the Aerostack and Aerostack2 frameworks for aerial robotics, which address execution control, mission planning, and sensor fusion challenges.

Saurav Agarwalمشاهده پروفایل

پژوهشگر

• Robotics
• Artificial Intelligence
• Computer Science
• +۱ مورد دیگر

Saurav Agarwal is a Postdoctoral Researcher affiliated with MEAM and the Kumar Lab, focusing on robotics and intelligent systems. His work intersects advanced robotics, computer science, and applied mechanics, as evidenced by his contributions to publications like 3D Active Metric-Semantic SLAM under IEEE. Research interests include Simultaneous Localization and Mapping (SLAM) in robotics 3D spatial modeling and semantic analysis Active metric-semantic integration for autonomous navigation Applications of robotics in complex environments Interdisciplinary approaches combining mechanical engineering and computer science He is associated with the GRASP Lab, a leading robotics research facility, and participates in academic engagement through seminars and publications. His current role involves advancing robotics research and collaboration within the Kumar Lab.

Matteo Poggiمشاهده پروفایل

استادیار

• Stereo matching
• Multi-View Stereo
• Optical flow
• +۷ مورد دیگر

Matteo Poggi is a Tenure-Track Assistant Professor at the University of Bologna, where he teaches courses on Logic Circuits and Computer Architectures. His academic career is deeply rooted in computer vision and 3D perception, with a focus on stereo vision, depth estimation, and SLAM systems. Dr. Poggi earned his MSc and PhD degrees from the University of Bologna in 2014 and 2018 respectively, working on stereo vision under the supervision of Prof. Stefano Mattoccia. His educational background has provided a strong foundation for his current research endeavors in computer vision and 3D scene understanding. Dr. Poggi's research primarily focuses on stereo matching, multi-view stereo, optical flow, single-image depth estimation, online adaptation, federated learning, and neural SLAM. His work bridges the gap between theoretical computer vision and practical applications, with particular emphasis on robustness in real-world scenarios. His recent publications demonstrate a growing interest in 3D Gaussian splatting, neural radiance fields, and environmental monitoring applications like river plastic detection. His scholarly output shows a consistent trajectory of high-impact publications at top computer vision conferences including CVPR, ICCV, and ECCV. The trend in his work reveals an evolution from traditional stereo vision techniques toward more advanced neural representations and applications in environmental monitoring. His research has significant implications for autonomous systems, robotics, and environmental science. Area Chair for CVPR 2026 Associate Editor for IJCV Outstanding Reviewer at CVPR 2025 Dr. Poggi actively collaborates with researchers across the globe, as evidenced by his extensive publication record. His GitHub profile shows active engagement with the research community through open-source implementations of his work. While specific grant information isn't provided in the available texts, his numerous publications in top venues suggest successful funding for his research endeavors. His work appears to be conducted within a vibrant research group at the University of Bologna, with connections to other researchers in the computer vision community as seen through his GitHub followers and collaborators. His research has practical applications in autonomous driving, robotics, and environmental monitoring.

• Robotics Perception
• Computer Vision
• Lidar and Radar Sensing
• +۵ مورد دیگر

Pascal Vasseur is a University Professor at the University of Picardie Jules Verne (UPJV) in the Faculty of Sciences, Computer Science Department. He leads the Robotics Perception group within the MIS Laboratory (Modeling, Information and Systems) and serves as a member of the National University Council (Section 61) since 2016. Additionally, he has been an Associate Editor for IEEE Robotics and Automation Letters since 2016, demonstrating his significant standing in the robotics research community. Professor Vasseur's research focuses on robotics perception systems, with particular expertise in vision (including omnidirectional and event-based cameras), Lidar, and Radar technologies. His work addresses fundamental challenges in pose estimation, mapping, and localization for robotics and automotive applications. His recent publications show a strong emphasis on advanced sensor technologies, multi-sensor calibration techniques, and practical implementations for autonomous systems. His publication record demonstrates consistent high-impact research output, with numerous articles in top robotics and computer vision venues including IEEE Robotics and Automation Letters, IEEE Transactions on Intelligent Vehicles, and CVPR. He has also authored two comprehensive books on Omnidirectional Vision (2023-2024), establishing himself as a leading expert in this specialized area of computer vision. Professor Vasseur has coordinated multiple significant research projects including ANR projects CaViAR and pLaTINUM, the international DrAACaR project, and PHC STAR and AMADEUS projects. He currently serves as scientific officer for the ANR CLARA Project, continuing his leadership in advancing robotics perception research. His research group actively contributes to solving practical challenges in automotive vision systems, drone navigation, and forest environment mapping, with applications spanning autonomous vehicles, robotics navigation, and environmental monitoring systems. The group's work bridges theoretical advances in computer vision with real-world robotics implementations.

Thanh Nguyen Canhمشاهده پروفایل

مدرس

• Simultaneous Localization and Mapping (SLAM)
• Semantic SLAM
• Active SLAM
• +۷ مورد دیگر

Thanh Nguyen Canh is a Lecturer at University of Engineering and Technology, Vietnam National University (UET VNU) and Teaching/Research Assistant at Japan Advanced Institute of Science and Technology (JAIST), School of Information Science. Primary affiliation is with the Robotics Lab at both institutions where he conducts research in SLAM systems and computer vision. PhD in Information Science at JAIST (2024.10-present) MS in Information Science at JAIST (2022.9-2024.9) BS in Robotics Engineering at UET VNU (2018.8-2022.8) Research focuses on advancing SLAM technologies through semantic understanding and active exploration. Key specialties include Visual-inertial odometry , probabilistic semantic mapping , and multi-sensor fusion for UAV navigation. Current projects integrate deep learning with traditional SLAM pipelines to create robust environmental representations. Publication activity centers on practical robotics applications, with the 2023 ICCAIS paper demonstrating object-oriented semantic mapping for UAV navigation. Research output emphasizes implementable solutions with active GitHub repositories showing continuous development in SLAM systems and reinforcement learning. Supervision involves teaching robotics curriculum at UET while mentoring research assistants at JAIST. Current projects provide hands-on experience with ROS, point cloud processing, and deep learning frameworks. Laboratory work occurs within the Robotics Lab environment at JAIST/UET, utilizing GitHub-hosted tools like probabilistic_semantic_mapping and S3M_SLAM for collaborative development. The lab maintains strong focus on real-world robotics applications with particular emphasis on aerial vehicle navigation systems.

Reiner Marchthalerمشاهده پروفایل

استاد

• Embedded Systems
• Sensor Data Fusion
• Kalman Filters
• +۲ مورد دیگر

Prof. Reiner Marchthaler is a Professor at Esslingen University of Applied Sciences within the Faculty of Computer Science and Information Technology. He serves as Deputy Director of the Institute for Intelligent Systems (IIS), Scientific Director of the Green IT 2026 Conference, and Liaison Lecturer for the Friedrich Ebert Foundation. His academic leadership spans autonomous systems research and educational initiatives in embedded technologies. His research centers on Embedded Systems and Sensor Data Fusion, with pioneering work on Kalman filters for autonomous systems. He maintains the authoritative resource kalman-filter.de and has developed real-time capable SLAM algorithms, camera-based reference systems, and parking space detection frameworks. His expertise extends to entropy-based safety evaluation in autonomous driving and semantic segmentation using mixed real/synthetic data. Analysis of his 2020-2025 publications reveals dominant trends in autonomous driving systems, emphasizing real-time sensor fusion, deep learning for perception, and safety validation. Key subfields include adaptive Kalman filtering (ROSE-Filter), landmark-based navigation, neural network training with synthetic data, and maximum entropy safety frameworks. His work bridges theoretical innovation with automotive applications, particularly in model vehicle testing environments. Prof. Marchthaler leads research at the Institute for Intelligent Systems, directing the Green IT 2026 initiative and advising the Friedrich Ebert Foundation. His team develops ROS-based validation environments for autonomous algorithms and maintains the Kalman filter knowledge portal. Current projects focus on connected traffic systems using conventional infrastructure landmarks and entropy-optimized safety protocols for production vehicles.