Papers
9
Total Citations
89
H-Index
5
About
James Riordan is a leading researcher in autonomous robotics, specializing in multi-sensor fusion, state estimation, and real-time perception for unmanned aerial and underwater vehicles. His work bridges the gap between robust navigation in GPS-denied environments and practical deployment for inspection, surveillance, and maintenance tasks. Riordan’s most cited paper, “Real-Time Underwater StereoFusion” (2018, 24 citations), introduced a vision-based system enabling real-time 3D environmental sensing for underwater robotics—a critical advancement for resident vehicles operating in challenging subsea conditions. He further advanced underwater autonomy with a low-cost, low-maintenance vision-based localization system (2020, 19 citations), validated through offshore trials. In aerial robotics, Riordan’s LGVINS framework (2024, 13 citations) integrates LiDAR, GPS, visual, and inertial data for smooth UAV state estimation, while his LSAF-LSTM approach (2025, 12 citations) uses self-adaptive deep learning to maintain robust performance in adverse environments. His collaborative work on cooperative UAV-USV bridge inspection (2024) highlights his commitment to real-world infrastructure monitoring. With over 89 total citations, Riordan’s research is shaping the future of resilient, multi-domain autonomous systems.
Research Focus
Key Achievements
Top Papers
- 1Real-Time Underwater StereoFusion24 citations · 2018
- 2Vision-Based Localization System Suited to Resident Underwater Vehicles19 citations · 2020
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- 5Multi-Sensor Fusion for Efficient and Robust UAV State Estimation7 citations · 2024
- 6Multi-mode Operations Marine Robotic Vehicle – a Mechatronics Case Study5 citations · 2010
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- 8Porting SYCL accelerated neural network frameworks to edge devices3 citations · 2023
- 9