Kandith Wongsuwan
Papers
2
Total Citations
25
H-Index
2
About
Kandith Wongsuwan is a robotics researcher whose work focuses on advancing autonomous navigation in challenging environments, particularly underwater and in unknown terrains. His primary research areas include visual odometry, simultaneous localization and mapping (SLAM), and embedded systems for robotics. Wongsuwan’s major contribution is the implementation of visual odometry estimation for autonomous underwater vehicles (AUVs) using low-cost, accessible hardware like the Raspberry Pi 2 and the Robot Operating System (ROS). This work, cited 20 times, demonstrates how to achieve robust underwater localization without expensive equipment, making autonomous exploration more feasible. He has also advanced SLAM methodology by generalizing corrective gradient refinement within the Rao-Blackwellized Particle Filter (RBPF) framework for occupancy grid LIDAR SLAM. This innovation improves mapping accuracy by reducing local minima issues, a critical challenge in robotic navigation. Though his citation counts are modest, Wongsuwan’s practical, open-source approach—combining ROS with affordable hardware—has inspired subsequent work in field robotics. His achievements highlight a commitment to democratizing advanced robotic capabilities, enabling students and researchers to experiment with autonomous systems in real-world, hazardous environments.
Research Focus
Key Achievements
Top Papers
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- 2