Find Academic Supervisors

Stefan LeuteneggerView profile

Assistant Professor

• Mobile Robotics
• SLAM (Simultaneous Localization and Mapping)
• Computer Vision
• +7 more

Prof. Stefan Leutenegger is a tenure-track Assistant Professor at Technische Universität München (TUM), leading the Machine Learning for Robotics group within the TUM School of Computation, Information, and Technology. Previously, he held roles as Senior Lecturer (2018–2021) and Lecturer (2014–2018) at Imperial College London's Dyson Robotics Lab, where he founded the Smart Robotics Lab. He earned his PhD (2014) from ETH Zurich, focusing on autonomous solar-powered aircraft navigation, and holds BSc (2006) and MSc (2009) in Mechanical Engineering from ETH Zurich. His research centers on mobile robotics, particularly enabling robots (e.g., drones) to perceive and navigate complex environments using machine learning and sensor data fusion. Key focus areas include SLAM, event-based vision, 3D reconstruction, and autonomous exploration. He has pioneered algorithms like BRISK (2011), OKVIS (2014), and ElasticFusion (2016), advancing real-time robotics perception. Notable Awards: Imperial College President's Award (2018), Best ECCV Paper (2016), ETH Medal for Dissertations (2015). Labs: TUM's Machine Learning for Robotics Group, Imperial's Smart Robotics Lab. Publications: Over 100 papers, including seminal works in CVPR, ECCV, and Robotics: Science and Systems. Current projects include DigiForests (forest inventory via robotics), aerial additive manufacturing, and object-centric semantic mapping. His work bridges theory and practice, with applications in autonomous drones, construction robotics, and human-robot interaction.

• Robotics
• Photogrammetry
• Simultaneous Localization and Mapping (SLAM)
• +12 more

Cyrill Stachniss is a Full Professor at the University of Bonn , where he heads the Lab for Photogrammetry and Robotics and is affiliated with the Lamarr Institute for Machine Learning and Artificial Intelligence . He was previously a Visiting Professor in Engineering at the University of Oxford until 2025. His academic journey includes positions at the University of Freiburg, University of Zaragoza, and the Swiss Federal Institute of Technology. University: University of Bonn School: Faculty of Engineering Department: Department of Photogrammetry Academic Rank: Professor His research spans robotics, photogrammetry, SLAM, autonomous navigation, perception systems, agricultural robotics, and unmanned aerial vehicles . He emphasizes probabilistic techniques for mobile robots and has made significant contributions to visual and LiDAR-based localization, scene understanding, and 3D reconstruction. The recent publications highlight a strong trend toward neural implicit representations, agricultural phenotyping, radar-based perception, and active learning . His team develops robust systems for real-world deployment in dynamic and unstructured environments, particularly in precision farming and autonomous vehicles. Scientific Awards: IEEE RAS Early Career Award (2013) Microsoft Research Faculty Fellow (2010) 7th EURON Georges Giralt Award (2008) Multiple Best Paper Awards at ICRA, IROS, RSS, and RAL Faculty Teaching Award, University of Freiburg He has advised numerous students and leads the DFG Cluster of Excellence PhenoRob and Research Unit FOR 1505 Mapping on Demand . His lab has co-founded three startups, reflecting strong industry and societal impact. He also runs the educational video series 5 Minutes with Cyrill , explaining key robotics concepts.

Guillermo GallegoView profile

Professor

• Robotics
• Computer Vision
• Event-based Vision
• +5 more

Guillermo Gallego is a Professor of Robotic Interactive Perception at the Faculty of Electrical Engineering and Computer Science , Technische Universität Berlin , holding the Einstein Center Digital Future (ECDF) Professorship since 2019. His research bridges robotics , computer vision , and applied mathematics , focusing on optimization methods for interdisciplinary imaging and control problems. Education : PhD in Electrical and Computer Engineering (Georgia Tech, 2011), MS in Mathematics (Georgia Tech, 2009), MS in Electrical Engineering (Georgia Tech, 2007), MS in Mathematical Engineering (Universidad Complutense de Madrid, 2005). Gallego's work explores event-based vision to enhance robot perception through low-latency sensing and real-time 3D reconstruction . He previously held postdoctoral positions at the Institute of Neuroinformatics (University of Zurich/ETH Zurich) and Technical University of Madrid (Marie Curie Experienced Researcher). His interdisciplinary projects span applications in ocean remote sensing , autonomous driving , and space exploration . Key scientific awards include the Fulbright Fellowship (2005-2010) and Marie Curie Experienced Researcher (2011-2014). His recent publications focus on event camera algorithms for optical flow , SLAM , and noise estimation , reflecting his leadership in event-based vision research. Collaborations include institutions like University of Zurich , Georgia Tech , and University of Pennsylvania . Research Grants : Funded through ECDF and Marie Curie programs. Labs : Affiliated with the Einstein Center Digital Future and Institute of Neuroinformatics (Zurich/ETH Zurich).

• Computer Vision in Medical Imaging
• Machine Learning
• Medical Imaging Segmentation
• +3 more

Dr. Antonio Ortiz is a Researcher at the University of Bonn, affiliated with the Life and Medical Sciences Institute (LIMES) and the IRU Mathematics and Life Sciences group. He works under the supervision of Professors Alexander Effland and Jan Hasenauer. His research focuses on Computer Vision in Medical Imaging and Machine Learning applications. He holds a Ph.D. in Electric and Electronics Engineering from Cinvestav, Mexico (2023), a Master's in Computer Science from Cicese, Mexico (2019), and a Bachelor's in Mechatronic Engineering (2017). His research integrates Bayesian methods, deep learning, and optical flow techniques for cardiac MRI segmentation, visual-inertial SLAM systems, and 3D shape measurement. Recent work emphasizes adaptive algorithms for medical imaging and robotics applications. Publications span medical imaging, robotics, and materials science, with a focus on algorithmic innovation and interdisciplinary applications. No scientific awards are explicitly mentioned, but his work demonstrates strong academic contributions. His advising activities and grants are not detailed in the provided text. He collaborates within the Effland Lab and IRT Mathematics and Life Sciences team.

Daniele CattaneoView profile

Researcher

• Autonomous Robots
• Deep Learning for Localization
• Sensor Fusion
• +3 more

Dr. Daniele Cattaneo is a Junior Research Group Leader at the Robot Learning Lab (University of Freiburg, Germany). He specializes in autonomous robotics, deep learning for perception and localization, and sensor fusion. His research focuses on embodiment-agnostic and environment-agnostic systems for robots, with applications in autonomous driving and healthcare robotics. Education: Ph.D. in Computer Science, Università degli Studi di Milano-Bicocca (2016–2020) M.Sc. and B.Sc. in Computer Science, Università degli Studi di Milano-Bicocca (2013–2016, 2010–2013) Research Interests: His work addresses challenges in LiDAR-camera calibration, SLAM (Simultaneous Localization and Mapping), unsupervised domain adaptation, and multimodal fusion for robust perception. Key projects include CMRNext (LiDAR-camera matching), Syn-Mediverse (healthcare scene understanding), and Continual SLAM (long-term autonomy). Awards & Grants: He leads funded projects like AI-Drive (next-gen autonomous driving algorithms) and iSUOR (operating room video analysis). Collaborations include work with the AIS Group and Robotic Learning Lab . Students & Labs: Supervises 12+ students in topics like LiDAR localization, HD maps, and radar-based navigation. Active in the Robot Learning Lab at Freiburg, contributing to open-source datasets and tools for robotics research.

• Autonomous Systems
• Computer Vision
• Robotics
• +3 more

Markus Enzweiler serves as Professor of Computer Science and Autonomous Systems and Director of the Institute for Intelligent Systems at Esslingen University of Applied Sciences, Germany, within the Department of Computer Science and Engineering. His leadership role positions him at the forefront of intelligent systems research in applied academic settings. His research spans autonomous systems, computer vision, and robotics with emphasis on visual-inertial SLAM, semantic segmentation, and collective perception. Key contributions address real-world challenges in unstructured environments like agricultural fields and urban settings through efficient perception systems. Recent work focuses on lightweight monocular solutions, sensor fusion techniques, and computational efficiency optimization for autonomous vehicles. Analysis of his 2024-2025 publications reveals strong trends in collective perception infrastructure, NeRF/Gaussian Splatting integration for SLAM, and multi-sensor dataset development. His research consistently benchmarks computational costs against accuracy improvements while creating valuable resources like the OPNV public transportation dataset and Rover multi-season SLAM corpus. As Director of the Institute for Intelligent Systems, Enzweiler leads initiatives advancing autonomous driving technologies through practical implementations and industry-relevant research frameworks.

Norbert HaalaView profile

Adjunct Professor

• Computer Vision
• Geoinformatics
• 3D Data Reconstruction
• +5 more

Norbert Haala is an Adjunct Professor and Deputy Director at the University of Stuttgart's Department of Photogrammetric Computer Vision. He leads the Research Group in Photogrammetric Computer Vision and is involved in ISPRS Working Group II/2 (Point Cloud Generation) and EuroSDR Commission 2 (Modelling and Processing). His work focuses on 3D data collection, SLAM algorithms, LiDAR integration, and UAV-based geospatial technologies. Research interests include image-based data collection, photogrammetric computer vision, and semantic segmentation of 3D point clouds. He has contributed to benchmarks like Hessigheim 3D and TIME, and developed tools like SURE for dense image matching. Teaching includes courses on digital image processing, terrain modeling, and computer vision. His publications emphasize real-time mapping, hybrid georeferencing, and neural surface reconstruction from airborne imagery. Projects involve UAV monitoring, indoor SLAM for robotics, and multi-modal data fusion for urban modeling. He collaborates on EuroSDR initiatives and publishes regularly in remote sensing and robotics domains.

Claus BrennerView profile

Adjunct Professor

• LiDAR Mapping
• Point Cloud Processing
• Robust Estimation
• +3 more

apl. Prof. Dr.-Ing. Claus Brenner is an Adjunct Professor at the Institute of Cartography and Geoinformatics within the Faculty of Civil Engineering and Geodetic Science at Leibniz University Hannover. His research focuses on LiDAR mapping, point cloud processing, and robust estimation, with applications in autonomous systems, urban mapping, and disaster risk assessment. He leads the Graduiertenkolleg 2159 research group on integrity and collaboration in dynamic sensor networks. Key research areas include 3D reconstruction, SLAM (Simultaneous Localization and Mapping), semantic segmentation of mobile mapping data, and cooperative perception systems. His work integrates advanced machine learning techniques with geospatial data analysis, addressing challenges in sensor fusion, uncertainty modeling, and real-time localization. Recent publications span topics like voxel-based point cloud localization for smart spaces, flood risk mapping using LiDAR, and adversarial shape completion. Brenner has contributed to benchmark datasets such as LuCoop and LUMPI, advancing research in cooperative perception and urban navigation. His methods emphasize robustness and scalability, often leveraging generative models and statistical frameworks for urban environment analysis. Notable projects include the development of high-definition mapping using LiDAR, trajectory-based road network reconstruction, and semantic annotation from user trajectories. His work bridges theoretical advancements in computer vision with practical applications in autonomous systems and smart infrastructure.

Le ChenView profile

Researcher

• Robotics
• Machine Learning
• Reinforcement Learning
• +5 more

Le Chen is a Doctoral Researcher at the Empirical Inference Department of the Max Planck Institute for Intelligent Systems and ETH Zurich , advised by Prof. Bernhard Schölkopf and Prof. Dieter Büchler. Previously, he obtained his M.S. in Electrical Engineering and Information Technology from ETH Zurich and gained research experience at Microsoft Mixed Reality & AI Lab, Tencent AI Lab, and Tencent Robotics X Lab. Research Interests: Le Chen focuses on the intersection of robotics and machine learning, with a specific emphasis on reinforcement learning for dexterous manipulation, visual-inertial calibration, and uncertainty-aware robotic perception. His work spans dynamic motion control, policy gradient subspaces, and novel algorithms for 3D/4D reconstruction. Key Contributions: He co-developed the RP1M dataset for bimanual piano manipulation and contributed to tendon-driven robot design ( Safe & Accurate ) and LEAP-VO for robust visual odometry. His research also includes Gaussian splatting dynamics for 4D content creation ( GaussianFlow ). Scientific Awards: Best Systems Paper Finalist at RSS 2024 for the RP1M dataset

• GNSS (Global Navigation Satellite Systems)
• Indoor Positioning Systems
• Pseudolite Technology
• +7 more

Baoguo Yu is a Professor at the Department of Navigation Science and Technology within the School of Geodesy and Geomatics at Wuhan University. He has established himself as a leading researcher in the field of navigation and positioning systems, with over 70 publications spanning from 2016 to 2025 across prestigious journals including IEEE Transactions on Instrumentation and Measurement, Sensors, and IEEE Access. His research interests primarily focus on GNSS (Global Navigation Satellite Systems), indoor positioning technologies, pseudolite systems, and signal processing for navigation applications. Professor Yu's work demonstrates a strong emphasis on practical implementations, particularly in challenging environments where traditional GNSS signals are degraded or unavailable. His recent publications reveal a growing integration of machine learning techniques with traditional navigation approaches, reflecting the evolving nature of the field. Analysis of his 15 most recent publications shows a clear research trajectory toward high-precision positioning solutions, with particular attention to indoor navigation challenges, LEO satellite navigation augmentation, and robust positioning under interference conditions. His work spans theoretical algorithm development to practical implementations for smartphones, unmanned vehicles, and specialized navigation equipment. Professor Yu maintains extensive collaborations with researchers across China, particularly with scholars from Wuhan University and other prominent Chinese institutions. His frequent co-authors include Lu Huang, Xingli Gan, Heng Zhang, and Chuanzhen Sheng, suggesting well-established research teams working on complementary aspects of navigation technology. His research has significant applications in autonomous vehicles, pedestrian navigation, emergency response systems, and any domain requiring precise positioning where GPS signals are unreliable. The consistent publication output in high-impact journals demonstrates his active research program and continued contributions to advancing navigation science.

• Robotics
• Navigation Systems
• Sensor Fusion
• +5 more

Chan Gook Park is a prominent researcher in robotics, navigation systems, and sensor fusion with an extensive publication record spanning over two decades. His work primarily focuses on visual-inertial odometry, LiDAR-based navigation, indoor positioning systems, and applications in both terrestrial and space environments. Dr. Park's research interests encompass robotics, sensor fusion, computer vision, and navigation systems. His work demonstrates a consistent focus on developing robust algorithms for visual-inertial odometry under challenging conditions, including low-light environments and indoor settings. He has made significant contributions to pedestrian navigation using wearable devices, lunar landing navigation systems, and infrared target detection. Analysis of his recent publications (2023-2025) reveals a strong trend toward deep learning integration in traditional navigation systems, with increasing focus on event-based vision sensors, multi-sensor fusion techniques, and applications in wearable technology. His work spans both theoretical algorithm development and practical implementations for real-world navigation challenges. Dr. Park has mentored numerous researchers who frequently appear as first authors on his publications, including Jae Hong Lee, Hanyeol Lee, and In Ho Lee. His collaborative network extends across various Korean institutions, with frequent co-authorship patterns suggesting a well-established research laboratory focused on navigation and sensor systems. His research group appears to maintain strong connections with the aerospace and robotics communities, regularly publishing in top-tier IEEE conferences and journals including IEEE Transactions on Robotics, IEEE Access, and proceedings from ICRA and PLANS conferences. The laboratory seems particularly focused on practical implementations of navigation systems for both terrestrial and space applications.

• Computer Vision
• Machine Learning
• Visual SLAM
• +3 more

Xin Su is a scientific staff member at the Chair of Media Technology, Technical University of Munich (TUM). He earned his B.Sc. in Electrical Engineering and Information Technology (ETIT) from Karlsruhe Institute of Technology (KIT) in 2019 and his M.Sc. in Electrical Engineering and Information at TUM in 2022. Since January 2023, he has been working on the 6G-ANNA project. Research Focus: Visual SLAM, sensor fusion, and machine learning for robotics. Projects: Involved in initiatives like 6G-ANNA, Centre for Tactile Internet (CeTI), and DFG Teleoperation over 5G. Publications: Recent work includes frameworks for visual-inertial odometry, CAD model retrieval, and point cloud registration, reflecting expertise in robotics and 3D perception. Collaborations: Co-authored papers with researchers such as Adam Misik, Driton Salihu, and Eckehard Steinbach.

Niclas VödischView profile

Researcher

• Continual Learning for Robotics
• Machine Perception
• Simultaneous Localization and Mapping (SLAM)
• +3 more

Niclas Vödisch is a Ph.D. student and researcher at the Autonomous Intelligent Systems Lab in the Department of Computer Science at the University of Freiburg. Supervised by Prof. Dr. Wolfram Burgard and co-supervised by Prof. Dr. Abhinav Valada, he is actively contributing to the field of robotics and AI as a member of the ELLIS Society. His research focuses on enhancing machine perception and SLAM systems using deep learning methods, with primary applications in mobile robotics and autonomous driving. Education: Ph.D. Student at University of Freiburg (June 2021 - June 2025, thesis submitted, defense pending) Visiting Ph.D. Student at University of Zurich (June 2024 - December 2024) M.Sc. in Computational Science and Engineering at ETH Zurich (September 2018 - May 2021) Visiting Undergraduate Student at Carnegie Mellon University (August 2016 - May 2017) B.Sc. in Computational Engineering Science at RWTH Aachen University (September 2014 - June 2018) Vödisch's research interests center around Continual Learning for Robotics, Machine Perception, and Simultaneous Localization and Mapping (SLAM). His work addresses the critical challenge of reducing dependency on extensive annotated training data in robotic perception systems. Through innovative approaches leveraging foundation models, he has developed methods that achieve high performance with minimal supervision, making robotic systems more adaptable to real-world environments. His research spans theoretical advances in deep learning and their practical implementation in autonomous systems. An analysis of his publication record reveals a clear progression from foundational SLAM techniques to sophisticated continual learning frameworks and foundation model applications. A unifying theme across his work is data efficiency in robotic perception, with increasing emphasis on collaborative multi-agent systems and cross-modal information integration for robust performance in challenging conditions. His recent publications demonstrate growing influence in the robotics community, evidenced by the IROS 2024 Best Paper Award for his BEVCar work. Scientific Awards: IROS 2024 Best Paper Award on Cognitive Robotics (BEVCar) IROS 2024 Best Student Paper Award Finalist (BEVCar) Dean's List at Carnegie Mellon University (fall 2016) DAAD full scholarship for CMU studies (2016-2017) Niclas has mentored numerous Master's students on projects spanning LiDAR panoptic segmentation, collaborative scene graph generation, and autonomous driving systems. His teaching portfolio includes co-organizing seminars on Robot Learning and Learning with Limited Supervision, as well as leading the FreiCAR practical autonomous driving course across multiple semesters. His research has received substantial funding from the German Research Foundation (DFG) Emmy Noether Program, NVIDIA academic grants, and Qualcomm Technologies Inc., reflecting the significance and potential impact of his work. As a key contributor to the Autonomous Intelligent Systems group at Freiburg, Vödisch collaborates closely with the Robot Learning group and has extended his research network through his visiting position at the University of Zurich's Robotics and Perception Group. His interdisciplinary approach bridges computer vision, robotics, and machine learning, positioning him at the forefront of research in AI-powered autonomous systems with practical real-world applications.

• Computer Science
• Electrical Engineering
• Machine Learning
• +5 more

Haobo Wang is a researcher affiliated with Zhejiang University , specifically within the School of Software Technology under the College of Computer Science and Technology . His work bridges Computer Science and Electrical Engineering , focusing on Machine Learning , Signal Processing , and Remote Sensing .

Torsten SattlerView profile

Research Professor

• 3D computer vision
• Machine learning
• Image-based localization
• +10 more

Torsten Sattler is a Senior Researcher at the Czech Institute of Informatics, Robotics and Cybernetics (CIIRC) at the Czech Technical University in Prague (CTU), where he heads the Spatial Intelligence group. Previously, he was a tenured Associate Professor at Chalmers University of Technology in Sweden and spent five years as a PostDoc and Senior Researcher at ETH Zurich in Switzerland. He received his PhD from RWTH Aachen University in Germany. His research focuses on the intersection of 3D computer vision and machine learning, with specific interests in image-based localization, 3D mapping and reconstruction, neural scene representations, and applications in robotics and AR/VR. He aims to make localization and mapping algorithms more robust by incorporating higher-level scene understanding. Dr. Sattler has published extensively at top computer vision conferences including CVPR, ICCV, and ECCV, with recent papers focusing on robust visual localization in changing environments, neural scene representations, and 3D reconstruction. His work has direct applications in autonomous systems and augmented reality. Best Paper Candidate at CVPR 2021 Best Paper Award at Photogrammetric Image Analysis 2019 Multiple Outstanding Reviewer Awards from 2015-2021 Ranked among top-10 computer scientists in Czech Republic by Research.com He currently supervises five PhD students and has served in various leadership roles including program chair for ECCV 2024, general chair for 3DV 2022, and area chair for multiple major conferences. His lab benefits from connections to the RICAIP Centre, one of the largest EU projects in AI and Industry 4.0, providing access to state-of-the-art infrastructure and industrial collaborations.