Daniel Riordan
Papers
8
Total Citations
270
H-Index
7
About
Daniel Riordan is a researcher whose work sits at the dynamic intersection of robotics, computer vision, and precision agriculture. His scholarship is primarily focused on autonomous mobile robot navigation, localization, and the application of deep learning and 3D vision to real-world challenges in industrial and agricultural settings. Riordan's most influential contribution, "Path Planning Techniques for Mobile Robots: A Review" (2020), has garnered 83 citations and stands as a comprehensive resource for researchers entering the field. His closely related work on autonomous factory navigation (54 citations) and adaptive multimodal localization techniques further cements his reputation as a leading voice in intelligent robotic systems. Notably, Riordan has bridged the gap between robotics and agriculture, with his 2019 review of 3D vision for precision dairy farming (52 citations) demonstrating how cutting-edge sensing technologies can transform animal husbandry practices. His exploration of deep learning for unmanned ground vehicles and computer vision for 3D perception reflects a forward-thinking approach to embodied AI. With a cumulative body of work exceeding 270 citations, Riordan's research offers both theoretical grounding and practical insight for students and practitioners working at the frontier of autonomous systems.
Research Focus
Key Achievements
Top Papers
- 1Path Planning Techniques for Mobile Robots A Review83 citations · 2020
- 2Autonomous Navigation of mobile robots in factory environment54 citations · 2019
- 33D Vision for Precision Dairy Farming52 citations · 2019
- 4Deep Learning for Visual Navigation of Unmanned Ground Vehicles : A review24 citations · 2018
- 5
- 6Where am I? Localization techniques for Mobile Robots A Review16 citations · 2020
- 7Computer Vision for 3D Perception16 citations · 2018
- 8