Prarinya Siritanawan
Papers
3
Total Citations
77
H-Index
3
About
Prarinya Siritanawan is a robotics researcher specializing in multimodal sensor fusion, 3D perception, and autonomous navigation. His work addresses critical challenges in enabling robots to perceive and understand complex environments through the integration of heterogeneous sensors. Siritanawan’s most influential contribution is a novel two-step method for extrinsic calibration between a sparse 3D LiDAR and a thermal camera, which overcomes the limitations of high-resolution sensors by reliably estimating 6-DOF parameters. This work, with 38 citations, has significant implications for autonomous robots operating in low-visibility or nighttime conditions. He further advanced the field with a knowledge-based multimodal information fusion framework for role recognition and situation assessment, cited 30 times, enabling mobile robots to interpret human activities and environmental context. Additionally, Siritanawan developed a 3D feature point detection algorithm using Multiresolution Surface Variation (MSV) for SLAM on sparse, non-uniform point clouds, demonstrating robust performance in cluttered, unstructured settings. His research directly impacts the reliability and autonomy of field robots, particularly in search-and-rescue, surveillance, and industrial inspection applications.
Research Focus
Key Achievements
Top Papers
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