Sakol Udomsiri

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About

Sakol Udomsiri is a researcher whose work sits at the intersection of computer vision, robotics, and video compression. His key contributions focus on developing efficient video coding strategies for multi-robot systems, particularly in indoor search and rescue scenarios. In his most cited work, "Functionally Layered Video Coding Based on JP2K for Robot Vision Network" (2009), Udomsiri proposed an innovative approach to reduce data transmission by extracting only the minimum visual information robots need to navigate and map their environment. The method involves decomposing ceiling video into functionally layered components using JPEG 2000, allowing search robots to share a common ceiling map while minimizing bandwidth consumption. Although his citation count (3) is modest, the work addresses a critical challenge in cooperative robotics: how to maintain situational awareness without overwhelming network capacity. Udomsiri's research is particularly notable for its practical orientation—rather than pursuing theoretical compression limits, he focused on what information is truly necessary for robot navigation. This pragmatic approach to vision-based robotics continues to be relevant as autonomous systems increasingly rely on bandwidth-constrained wireless networks for collaborative perception and mapping.