جست‌وجوی استاد و پژوهشگر

Daniele Cattaneoمشاهده پروفایل

پژوهشگر

• Autonomous Robots
• Deep Learning for Localization
• Sensor Fusion
• +۳ مورد دیگر

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.

Aljaž Božičمشاهده پروفایل

پژوهشگر

• Computer Vision
• Computer Graphics
• Artificial Intelligence
• +۷ مورد دیگر

Aljaž Božič is a Research Scientist at Meta Reality Labs Research , focusing on neural rendering, 3D reconstruction, and AI-driven geometry modeling. He earned his Ph.D. in Computer Science from the Technical University of Munich (TUM) and holds a Master's in Computer Science from TUM and a Bachelor's in Mathematics from the University of Ljubljana . His research spans computer vision, graphics, and artificial intelligence , with a focus on neural rendering , generative AI , and 3D deformable object modeling , targeting applications in VR/AR and robotics. His work includes time-consistent dynamic scene reconstruction (SceNeRFlow), volumetric hair appearance modeling, and high-fidelity walkable VR spaces (VR-NeRF), alongside efficient NeRF distillation and calibration methods (Neural Lens Modeling). Key article trends include Transformer-based monocular reconstruction (TransformerFusion), neural parametric shape models (NPMs), and self-supervised non-rigid tracking (Neural Deformation Graphs). He has contributed to open-source projects like the TransformerFusion GitHub repository , emphasizing MIT-licensed tools for scene reconstruction. At TUM, he served as a Teaching Assistant for courses such as 3D Scanning and Spatial Learning and 3D Vision Seminar , bridging academic instruction with research innovation. His work integrates advanced neural networks with practical optimization techniques, advancing fields like RGB-D reconstruction (DeepDeform) and variational SLAM.

Alexandre Mendesمشاهده پروفایل

مدرس ارشد

• Machine Learning
• Optimization
• Deep Learning
• +۶ مورد دیگر

Dr. Alexandre Mendes is a Senior Lecturer at the University of Newcastle, affiliated with the School of Information and Physical Sciences and the Department of Computing and Information Technology. His research focuses on machine learning, optimization, and deep learning, with applications in robotics, energy systems, and transport logistics. Education: PhD in Electrical Engineering, Universidade Estadual de Campinas, Brazil MSc in Electrical Engineering, Universidade Estadual de Campinas, Brazil Bachelor in Applied Mathematics, Universidade Estadual de Campinas, Brazil Graduate Certificate in the Practice of Tertiary Teaching, University of Newcastle Research Interests: Mendes specializes in applying machine learning to real-world problems such as optimizing railway logistics, predicting solar energy output, and improving robotic systems. His work bridges theory and practice, collaborating with industries like CSIRO and Hunter Valley Coal Chain. Grants and Funding: He has secured over AU$1.5M in grants from ARC, CSIRO, and industry partners. Key projects include coal train scheduling optimization and solar energy prediction using deep learning. Awards: Work Integrated Learning Staff Member of the Year (2020) Best Student Paper Award (2012) Pro Vice-Chancellor's Research Excellence Award (2011) Labs and Teams: He leads the NUbots robotics team, focusing on humanoid robots and autonomous systems. His research group collaborates on projects like the Sydney Metro and Hunter Region rail network optimization.

• Robotics
• Simultaneous Localization and Mapping (SLAM)
• Sensor Fusion
• +۵ مورد دیگر

Maurice Fallon is a Professor of Engineering Science at the University of Oxford and a Royal Society University Research Fellow, leading the Dynamic Robot Systems Group (Perception) at the Oxford Robotics Institute. His research focuses on robust probabilistic methods for localization and mapping in challenging environments through advanced sensor fusion. Education: Electronic Engineering, University College Dublin PhD in Acoustic Source Tracking, University of Cambridge Research Interests: Dr. Fallon specializes in probabilistic state estimation , legged robot navigation , and dynamic motion planning for autonomous systems operating in vision-denied or complex natural environments. His work emphasizes robustness through multi-sensor integration , with applications spanning disaster response, forestry, and industrial inspection. Key innovations include terrain-aware locomotion and long-term autonomy frameworks. Publication Trends: Recent work (2024-2025) demonstrates a strategic shift toward forest robotics and long-term industrial inspection , leveraging legged and aerial platforms. There is strong emphasis on vision foundation models for place recognition, scalable 3D reconstruction using neural radiance fields, and open-vocabulary scene understanding . The research consistently addresses real-world challenges like lighting variations, sensor dropout, and environmental dynamics. Scientific Awards: Royal Society University Research Fellowship 4x Best Paper Awards at ICRA Nominations at Intelligent Vehicles, AAAI, and Humanoids conferences Advising and Grants: Dr. Fallon has secured major funding as PI/Co-I for EU/UK projects including ORCA, RAIN, THING, MEMMO, and the DARPA SubT-winning CERBERUS team. Current initiatives include the Horizon Europe DigiForest project and UKAEA collaborations. He mentors PhD students and postdocs in robotics systems development, though specific advisees aren't listed in source materials. Labs and Teams: He directs the Dynamic Robot Systems Group, which achieved global recognition through DARPA Robotics Challenge participation and SubT Challenge victory. The team operates specialized facilities for legged robot testing and maintains partnerships with nuclear energy and forestry sectors for field deployment.

• Computer Vision
• Robotics
• Medical Imaging
• +۳ مورد دیگر

Adlane HABED is an Associate Professor of Computer Science at Télécom Physique Strasbourg, University of Strasbourg, France. He is affiliated with the Control, Vision and Robotics research group within the Imaging, Computer and Engineering Sciences Laboratory (ICube UMR-CNRS 7357). His work focuses on advanced computer vision techniques, medical imaging applications, and robotics, with a strong emphasis on camera calibration, 3D reconstruction, and sensor fusion. Research interests include: - Robust camera autocalibration methods - 3D reconstruction from uncalibrated data - Medical applications in laparoscopic surgery and image-guided interventions - Optimization techniques for vision systems (e.g., LMI/SOS programming) - Sensor fusion for robotics and visual odometry Notable projects include DrAACaR, ArchZoo3D, MOSCA, and WiDop, though specific project descriptions are not provided. His work bridges theoretical computer vision with practical medical and robotic applications. Labs/Teams: Active member of the ICube laboratory's Control, Vision and Robotics group, contributing to interdisciplinary research in autonomous systems and medical imaging.

Claus Brennerمشاهده پروفایل

استاد مدعو

• LiDAR Mapping
• Point Cloud Processing
• Robust Estimation
• +۳ مورد دیگر

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.

• Robotics
• Biomedical Engineering
• Control Systems
• +۳ مورد دیگر

Jason Gu is a Professor in the Department of Electrical and Computer Engineering at Dalhousie University, cross-appointed to the School of Biomedical Engineering. His research integrates robotics, control systems, and biomedical engineering to develop innovative solutions for mobile robotics, surgical systems, and rehabilitation technologies. His primary research domains include: Robotics : Mobile robotics, surgical robots, rehabilitation assistive devices, wireless control systems, and multi-sensor data fusion. Biomedical Engineering : Artificial eye implant control, medical robotic devices, and rehabilitation technology design. Control Systems : Real-time intelligent control, nonlinear systems theory, and embedded control applications. Alternative Energy : Development of novel energy technologies and systems. Analysis of his recent publications (2023-2025) reveals a strong convergence of AI with robotics, particularly in vision-language models for human-robot interaction, semantic SLAM for dynamic environments, and medical image processing. His work shows increasing emphasis on lightweight algorithms for UAVs, neural interfaces, and energy-efficient control systems for aerospace applications. His distinguished honors include: IEEE Canada President (2020-2021) and President-elect (2018-2019) Fellow of the Engineering Institute of Canada (FEIC) Fellow of the Canadian Academy of Engineering (FCAE) Professional Engineer (PEng) designation Professor Gu leads a dynamic research laboratory developing advanced robotic platforms including the PA10 Portable General-Purpose Intelligent Arm and B21r Mobile Robotic System. His team actively pursues real-world applications in surgical robotics, terrain perception for legged robots, and alternative energy systems through industry-academic partnerships and competitive research grants.

• 3D models
• Digital cultural heritage
• Digital photogrammetry
• +۱۰ مورد دیگر

Giulia Sammartano is a Fixed-term Tenure-Track Assistant Professor at the Department of Architecture and Design (DAD) of the Polytechnic University of Turin . She is a member of the FUTURE Urban Legacy Lab (FULL) and engages in interdisciplinary research at the intersection of geomatics, cultural heritage, and sustainable architecture. Scientific Branch: Geomatics (CEAR-04/A, Area 0008 - Civil Engineering and Architecture) ERC Skills: PE8_3 (Civil Engineering), PE6_8 (Computer Graphics), SH2_12 (GIS), PE2_15 (Metrology) SDG Contributions: Quality Education, Gender Equality, Cities & Communities, Climate Action Her research interests include 3D modeling , digital cultural heritage , UAV photogrammetry , and SLAM-based mapping . She specializes in applying advanced geomatic techniques for heritage documentation, with a focus on laser scanning , point cloud processing , and HBIM (Heritage Building Information Modeling). Recent publications highlight her work on the REVHEAL and HERITALISE projects, exploring topics like masonry vault simulations , autonomous robotic surveying , and ML/DL classification of LiDAR data . She serves as Guest Editor for journals such as Remote Sensing , Sensors , and Drones . Awards: Polytechnic University of Turin Publications Award (2016, 2018) Best Poster SIFET2017 (2017) Quality of Research Activity (2016) Scientific Memberships: ICOMOS Italia (2023-) CIPA Heritage Documentation (2023-) ISPRS (2020-)

Todor Stoyanovمشاهده پروفایل

مدرس ارشد

• Autonomous Mobile Robots
• Robot Perception Algorithms
• Motion Synthesis for Manipulation
• +۳ مورد دیگر

Todor Stoyanov is a Senior Lecturer in Computer Science and an affiliated faculty member of the WASP program at Örebro University. He serves as the subject manager in computer science and is part of the Centre for Applied Autonomous Sensor Systems (AASS). His research focuses on autonomy for mobile robots, particularly perception algorithms and motion synthesis for manipulation. He holds a PhD in Computer Science from Örebro University (2012), specializing in autonomous robot navigation. Education: PhD in Computer Science, Örebro University, 2012 (Thesis: Reliable Autonomous Navigation in Semi-Structured Environments Using the 3D Normal Distributions Transform) Research Interests: Dr. Stoyanov's work spans autonomous mobile robots, robot perception, motion planning, and manipulation. Key areas include behavior trees for control, deformable object tracking, and reinforcement learning for knowledge transfer. His research often integrates advanced algorithms with real-world robotics applications in logistics, manufacturing, and environmental monitoring. Research Projects: Ongoing: Labour market effects of AI in knowledge-intensive services Ongoing: Dynamic Agile Production Robots (DARKO) Ongoing: TeamRob - Teams of Robots Working for and with Humans Completed: Action and Intention Recognition in Human-Robot Interaction (AIR) Completed: Autonomous Wheeled Loaders for Material Handling (ALL-4-eHam) Labs/Groups: He leads the Autonomous Mobile Manipulation Lab, focusing on full-body mobile manipulation and human-robot collaboration.

Suchendra M. Bhandarkarمشاهده پروفایل

استاد

• Artificial Intelligence
• Computer Vision
• Pattern Recognition
• +۴ مورد دیگر

Suchendra M. Bhandarkar is a Professor in the School of Computing at the University of Georgia, affiliated with the Franklin College of Arts & Sciences. He holds courtesy faculty positions in the College of Engineering and the School of Environmental, Civil, Agricultural, and Mechanical Engineering. His research focuses on Artificial Intelligence, Computer Vision, Pattern Recognition, and Computational Biology. He has advised PhD students such as Anirban Mukhopadhyay and has been actively involved in interdisciplinary projects spanning robotics, coral reef mapping, and food safety automation. Education: Ph.D. and M.S. in Computer Engineering from Syracuse University (1985-1989), and B.S. in Electrical and Electronics Engineering from Indian Institute of Technology Bombay (1983). His funded projects include advanced GeoAI terrain analytics, underwater robotics, and AI-driven food safety systems. Notable grants include support from USDA, NIH, and the US Army. Research highlights include 3D coral reef mapping via SLAM, deep learning for food safety, and zero-shot learning algorithms. Key collaborations involve the USDA Agricultural Research Service, Sony Corporation, and international networks like CapsNet for medical imaging. His labs and teams focus on applying AI to environmental, biomedical, and agricultural challenges.

• Geomatics
• Cultural Heritage Documentation
• 3D Modeling
• +۱۵ مورد دیگر

Antonia Teresa Spano' is a Full Professor at the Department of Architecture and Design (DAD), Politecnico di Torino, where she leads research and teaching in geomatics for architectural and landscape heritage. She is a member of the Future Urban Legacy Lab (FULL) and serves as Deputy Coordinator of the PhD College in Architectural Heritage. Her work spans interdisciplinary collaborations with institutions such as the Archaeological Park of Pompeii, the Royal Museums of Turin, the University of Salento, and the Bruno Kessler Foundation. She has led numerous national and international research projects, including those funded by PRIN, EU, and commercial contracts. Her research focuses on the application of advanced geomatic technologies—including 3D modeling, UAV photogrammetry, LiDAR, SLAM-based mobile mapping, and GIS—to the documentation, analysis, and conservation of cultural heritage. She integrates computer vision, remote sensing, and digital reconstruction techniques to support sustainable urban development and heritage preservation. Her work is closely aligned with UN Sustainable Development Goals (SDGs) 4 (Quality Education), 5 (Gender Equality), and 11 (Sustainable Cities and Communities). The most recent publications reflect a strong trend in digital heritage, with emphasis on AI-driven decay detection, multispectral UAV data for landscape analysis, semantic classification of LiDAR data, and integrated 3D survey methods for heritage structures. Her work frequently involves collaborations with PhD students and international research teams, focusing on both built and archaeological heritage. Best poster, GEORES 2019 (ISPRS) Best poster, GISTAM 2016 CNR Youth Award, 2005 She is actively involved in advising PhD students and managing research grants, including scholarships funded by IREN Energia SPA and the Bruno Kessler Foundation. She serves as Scientific Director for multiple collaborative agreements with public and private institutions. She also contributes to major research projects such as the diagnosis of Pier Luigi Nervi’s exhibition halls and urban regeneration initiatives in Elva and Turin. She leads the LabG4CH – Geomatics Laboratory for Cultural Heritage – and participates in editorial boards of journals like Sensors , Techne , and Virtual Archaeology Review .

Nived Chebroluمشاهده پروفایل

پژوهشگر

• Robotics
• Autonomous Systems
• Simultaneous Localization and Mapping (SLAM)
• +۴ مورد دیگر

Nived Chebrolu is a Senior Research Associate at the Oxford Robotics Institute (ORI) , working with the Dynamic Robot Systems Group since 2021. His research focuses on robot navigation and mapping for field robots, particularly in forest and agricultural environments. University of Bonn – Ph.D. in Robotics Ecole Centrale de Nantes (France) & University of Genoa (Italy) – M.Sc. in Robotics (2015) Research Interests : Simultaneous Localization and Mapping (SLAM) with LiDAR and vision systems Robust registration techniques for long-term autonomy Perception systems for forest and agricultural robotics Autonomous aerial and terrestrial navigation Field robotics applications in precision agriculture Publications reflect expertise in: Forest point cloud co-registration Online tree reconstruction LiDAR-based place recognition Traversability estimation for wild environments Handheld LiDAR for real-time inventory Teaching Activities : Lecturer for M.Sc. course on Mobile Sensing and Robotics Instructor for Techniques for Self-Driving Cars course

• Robotics
• Computer Vision
• Simultaneous Localization and Mapping (SLAM)
• +۶ مورد دیگر

Niko Sünderhauf is a Professor in the School of Electrical Engineering and Robotics at Queensland University of Technology (QUT), specializing in robotics, computer vision, and autonomous systems. His research spans visual place recognition, simultaneous localization and mapping (SLAM), neural radiance fields, and out-of-distribution detection for robotics applications. His research interests focus on enabling robots to understand and navigate complex environments through advanced computer vision techniques. He has made significant contributions to visual place recognition, particularly for handling viewpoint and environmental changes. His work on switchable constraints has improved robustness in SLAM systems, while his more recent research explores neural radiance fields, Gaussian splatting, and integrating large language models with robotic perception. Prof. Sünderhauf's publication record demonstrates consistent research productivity with 115 publications spanning from 2005 to 2025. His recent work shows a clear progression from traditional SLAM techniques toward more advanced neural scene representations and integration with large language models. His research has been published in top venues including IEEE ICRA, CVPR, IROS, and the International Journal of Robotics Research. Among his scientific contributions are novel approaches to visual place recognition that work across seasons and viewpoints, robust SLAM techniques that handle outliers and non-linearities, and recent innovations in neural scene representations for robotics. His work on switchable constraints has been particularly influential in the robotics community. Prof. Sünderhauf actively supervises PhD students including Dimity Miller, Krishan Rana, and Jad Abou-Chakra, who frequently appear as first authors on publications. His collaborative network includes researchers from QUT and international institutions, demonstrating strong research leadership in the robotics community.

• Computer Vision
• Machine Learning
• Artificial Intelligence
• +۵ مورد دیگر

Dr. Chenyang Zhao is an Assistant Professor in the Department of Computer Science and Engineering at Shanghai Jiao Tong University's School of Electronic Information and Electrical Engineering. His research spans multiple domains at the intersection of computer vision, machine learning, and engineering applications. Dr. Zhao's primary research interests include: Computer Vision and Deep Learning Robotics and Autonomous Systems Medical Imaging and Healthcare Applications Hardware Acceleration for AI Precision Measurement and Instrumentation His recent work demonstrates a strong focus on practical applications of AI across diverse domains, from medical imaging to infrastructure inspection. Dr. Zhao has developed innovative approaches in areas such as sewer inspection systems using evidential deep learning, RGB-D semantic SLAM for robotics, and lightweight computing-in-memory architectures for edge AI applications. His research shows a consistent pattern of addressing real-world engineering challenges with cutting-edge machine learning techniques, particularly emphasizing trustworthiness, uncertainty quantification, and practical implementation constraints. Dr. Zhao's laboratory focuses on developing trustworthy AI systems with applications in: Infrastructure monitoring and maintenance Medical diagnostics and imaging Autonomous robotic systems Energy-efficient AI hardware

Ben Talbotمشاهده پروفایل

پژوهشگر ارشد

• Cognitive Robotics
• Artificial Intelligence
• Robot Navigation
• +۵ مورد دیگر

Dr. Ben Talbot is a Research Fellow at the Queensland University of Technology (QUT) , affiliated with both the Australian Centre for Robotic Vision (ACRV) and the QUT Centre for Robotics (QCR) . He earned a PhD in 2018 and a BEng (Honours I) in Electrical Engineering in 2012 from QUT. Ben specializes in cognitive robotics , artificial intelligence , and real-world robotic applications , with a focus on translating theoretical advances into practical systems. PhD , Robotics, Queensland University of Technology, 2018 BEng (Honours I) , Electrical Engineering, Queensland University of Technology, 2012 His research explores robot navigation using abstract maps , semantic SLAM , and human-inspired spatial reasoning , particularly in unseen environments . He pioneered the Abstract Map framework, which enables goal-directed exploration through symbolic spatial information. His work emphasizes open-source software like BenchBot and OpenSeqSLAM2.0 , enhancing robotics community accessibility. Ben's publications span 2013–2023, showcasing advancements in controller fusion , scene understanding , and sim-to-real transfer . Notable trends include integrating reinforcement learning with classical control and developing dynamic spatial models for navigation. Awarded Best Student Paper at ACRA 2015 and multiple Vice-Chancellor's Awards in 2015, Ben also received the Dean's Scholars Award in 2009. His work has been featured in The LABS and Brisbane Times media outlets. Beyond research, Ben contributed to robotics education as Head Tutor for Advanced Robotics and Microprocessors & Digital Systems during his PhD. He actively supports active scene understanding via high-fidelity simulated environments and robust API designs, aligning with QUT's commitment to bridging classical and modern robotics.