Konstantin Sofiiuk
Papers
1
Total Citations
3
H-Index
1
About
Konstantin Sofiiuk is a researcher whose work lies at the intersection of computer vision, robotics, and semantic scene understanding. His key contributions center on advancing simultaneous localization and mapping (SLAM) systems by integrating semantic information, enabling robots to not only navigate but also comprehend their environments. His most notable work, "DISCOMAN: Dataset of Indoor SCenes for Odometry, Mapping And Navigation," introduced a large-scale, realistic dataset comprising 200 long sequences with thousands of frames each, designed to train and benchmark semantic SLAM methods. This dataset, generated from real home layouts and robot-like trajectories, has become a foundational resource for researchers, accumulating over 3 citations and supporting the development of more intelligent, context-aware robotic systems. Sofiiuk’s efforts have helped bridge the gap between raw sensor data and high-level scene interpretation, making him a key figure in the push toward autonomous robots that can operate seamlessly in human environments. His work continues to inspire new approaches in indoor navigation and mapping.
Research Focus
Key Achievements
Top Papers
- 1DISCOMAN: Dataset of Indoor SCenes for Odometry, Mapping And Navigation3 citations · 2019