Papers
9
Total Citations
517
H-Index
7
About
Igor Bogoslavskyi is a leading researcher in mobile robotics and autonomous navigation, with a primary focus on real-time 3D perception from sparse LiDAR sensors. His most impactful contribution is the development of fast, online segmentation algorithms for 3D laser scans—his seminal 2016 paper on range image-based segmentation has garnered over 245 citations, establishing a foundational approach for object detection in dynamic environments. Building on this, his 2017 work on efficient online segmentation (131 citations) further refined these methods for practical deployment in autonomous cars and mobile robots. Beyond segmentation, Bogoslavskyi has advanced robust exploration and homing strategies for autonomous robots, as well as traversability analysis using low-cost sensors like the Kinect. His research also addresses critical challenges in 3D point cloud registration, including photometric alignment and normal estimation for sparse LiDAR data. With a total of over 500 citations across his publications, Bogoslavskyi’s work has directly influenced the development of reliable, real-time perception systems that enable robots to navigate and understand complex, unstructured environments.
Research Focus
Key Achievements
Top Papers
- 1
- 2Efficient Online Segmentation for Sparse 3D Laser Scans131 citations · 2017
- 3Robust exploration and homing for autonomous robots61 citations · 2016
- 4Efficient traversability analysis for mobile robots using the Kinect sensor32 citations · 2013
- 5Analyzing the quality of matched 3D point clouds of objects16 citations · 2017
- 6Exploration and mapping of catacombs with mobile robots12 citations · 2013
- 7Robust homing for autonomous robots11 citations · 2016
- 8
- 9Fast and Robust Normal Estimation for Sparse LiDAR Scans2 citations · 2024