Papers
3
Total Citations
48
H-Index
3
About
Tawsif Gokhool is a researcher specializing in robotics, computer vision, and 3D mapping, with a particular focus on point cloud registration and environmental representation for autonomous mobile robots. His most impactful contribution, the "CICP: Cluster Iterative Closest Point for sparse–dense point cloud registration" (2018), has garnered 38 citations, introducing a novel method that clusters points to improve the alignment of sparse and dense 3D data—a critical challenge in robotic perception and SLAM. Gokhool also advanced visual mapping through his work on spherical RGBD images, proposing a compact keyframe-based representation (2015, 7 citations) that integrates hybrid metric-topological maps for efficient navigation, and a dense map building approach (2014, 3 citations) that leverages visual odometry for pose graph construction. His research bridges theoretical innovation with practical applications in service and industrial robotics, offering students and researchers valuable insights into robust, real-time mapping systems. Gokhool’s work underscores the importance of scalable, ego-centric representations in enabling autonomous navigation in complex environments.
Research Focus
Key Achievements
Top Papers
- 1
- 2A compact spherical RGBD keyframe-based representation7 citations · 2015
- 3A Dense Map Building Approach from Spherical RGBD Images3 citations · 2014