Iuganov Egor
Papers
1
Total Citations
3
H-Index
1
About
Egor Iuganov is a roboticist specializing in autonomous navigation and simultaneous localization and mapping (SLAM) for legged robots in challenging, GPS-denied environments. His most cited work, "Online LiDAR-SLAM for Legged Robots with Robust Registration and Deep-Learned Loop Closure" (2020), introduces a 3D factor-graph LiDAR-SLAM system that integrates a deeply learned feature-based loop closure detector, enabling legged platforms to reliably localize and map in industrial settings. A key innovation is the use of an inertial-kinematic state estimator to accumulate point clouds before registration, significantly improving robustness during dynamic locomotion. This contribution addresses a critical gap in field robotics—achieving drift-free, real-time mapping on walking robots. With 3 citations, this paper has laid groundwork for subsequent advances in legged SLAM. Iuganov’s research bridges deep learning and classical estimation, pushing the boundaries of autonomous navigation for quadrupedal robots in complex, unstructured environments.
Research Focus
Key Achievements
Top Papers
- 1