Onur Akbulut
Papers
1
Total Citations
2
H-Index
1
About
Onur Akbulut is a researcher specializing in 3D perception, point cloud processing, and robotic perception systems, with a particular focus on indoor planar surface segmentation. His work addresses the critical challenge of enabling robots to interpret and navigate indoor environments using 3D laser point cloud data. In his most cited study, "A Comparative Study for Indoor Planar Surface Segmentation via 3D Laser Point Cloud Data" (2020, 2 citations), Akbulut systematically evaluates various segmentation algorithms for extracting planar surfaces from point clouds generated by RGB-D cameras and 3D LiDARs. This research is foundational for applications in autonomous navigation, mapping, and scene understanding, providing a benchmark for algorithm selection in resource-constrained robotic platforms. While his citation count is modest, his work contributes to the practical deployment of cost-effective RGB-D sensors in indoor robotics, bridging the gap between low-cost hardware and reliable perception. Akbulut’s research is particularly valuable for students and engineers developing robotic systems for structured indoor environments, offering insights into algorithm performance trade-offs that are essential for real-world implementation.
Research Focus
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Top Papers
- 1