Sho Komai
Papers
2
Total Citations
13
H-Index
2
About
Sho Komai is a robotics researcher whose work focuses on the intersection of robot calibration, computer vision, and simultaneous localization and mapping (SLAM) for indoor mobile robots. His most cited contributions include a 2009 study on calibrating kinematic parameters of robots using neural networks and laser tracking systems, which achieved 7 citations, and a companion paper on object detection and recognition that integrates template matching with SIFT features. In this latter work, Komai proposed a novel method for processing monocular images of entire environmental views to support SLAM, introducing the concept of invisible floor marks to modify the environment and narrow object search spaces. This approach addresses a key challenge in indoor robotics: ensuring reliable object detection during autonomous navigation. While his citation counts are modest, Komai’s contributions demonstrate practical engineering solutions that bridge neural network-based calibration and vision-guided navigation, offering foundational techniques for researchers working on low-cost, vision-based robotic systems in structured indoor environments.
Research Focus
Key Achievements
Top Papers
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- 2