Papers
70
Total Citations
4,230
H-Index
27
About
Kostas Daniilidis is a pioneering computer vision and robotics researcher whose work spans event-based vision, simultaneous localization and mapping (SLAM), autonomous navigation, and multi-robot coordination. Based at the University of Pennsylvania, he has made transformative contributions to how machines perceive and interact with the world. Daniilidis's survey on event-based vision (633 citations) has become a foundational reference in the field, illuminating how bio-inspired cameras can revolutionize motion sensing beyond conventional frame-based approaches. His work on semantic SLAM (474 citations) pushed the boundaries of robot localization by integrating probabilistic data association with high-level semantic understanding — a significant leap beyond purely geometric methods. He has also contributed critically to SLAM initialization techniques (238 citations) and decentralized active information acquisition (183 citations), addressing core challenges in robot autonomy. Beyond perception, Daniilidis has shaped multi-robot systems, developing vision-based control laws for nonholonomic robot formations (228 citations) and leader-follower coordination (181 citations). His contributions extend to autonomous UAV flight in GPS-denied environments (196 citations) and 3D object detection for robotic grasping (227 citations). With thousands of cumulative citations, his research has profoundly influenced both academic robotics and real-world autonomous systems development.
Research Focus
Key Achievements
Top Papers
- 1Event-Based Vision: A Survey633 citations · 2020
- 2Probabilistic data association for semantic SLAM474 citations · 2017
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- 5Single image 3D object detection and pose estimation for grasping227 citations · 2014
- 6Fast, autonomous flight in GPS‐denied and cluttered environments196 citations · 2017
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- 8Vision-Based Localization for Leader–Follower Formation Control181 citations · 2009
- 9Event-based feature tracking with probabilistic data association168 citations · 2017
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