Elene Firmeza Ohata
Papers
2
Total Citations
28
H-Index
2
About
Elene Firmeza Ohata is a robotics researcher whose work centers on advancing autonomous mobile robot localization and navigation through innovative sensor fusion and machine learning techniques. Her primary research areas include monocular vision systems, depth map estimation, and the integration of omnidirectional sonar imaging with artificial intelligence for robotic perception. Ohata's most notable contribution is her 2020 paper "Monocular Vision Aided Depth Map from RGB Images to Estimate of Localization and Support to Navigation of Mobile Robots," which has garnered 26 citations—a significant impact for a specialized robotics topic. This work proposes a novel approach that leverages single-camera RGB images to generate depth maps, addressing one of the most fundamental challenges in mobile robotics: accurate localization without expensive multi-sensor setups. Her earlier 2019 paper on omnidirectional sonar images combined with machine learning for localization, while less cited, demonstrates her sustained focus on cost-effective, intelligent solutions for robots operating in unknown environments. Ohata's research bridges computer vision, signal processing, and machine learning to create practical navigation aids, making her work particularly valuable for researchers developing autonomous systems for industrial, service, or exploratory robotics.
Research Focus
Key Achievements
Top Papers
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