Vladislav Kibalov
Papers
2
Total Citations
4
H-Index
2
About
Vladislav Kibalov is a researcher at the intersection of robotics and computed tomography, where he applies advanced estimation and optimization techniques to solve fundamental problems in sensor fusion and trajectory reconstruction. His primary research areas include robot localization, state estimation, and tomographic imaging, with a particular focus on refining motion models and reference trajectories. Kibalov’s major contributions are twofold: in TomoSLAM (2022, 2 citations), he introduced factor graph optimization to correct rotation angle errors in microtomography caused by mechanical backlash, effectively bridging simultaneous localization and mapping (SLAM) with CT imaging. In his 2023 work on Prior Distribution Refinement (2 citations), he developed a novel Monte Carlo-based method for generating accurate reference trajectories, enabling fairer comparison of robot localization algorithms. Though early in his career, Kibalov’s work demonstrates a rare ability to transfer robotics estimation frameworks into imaging domains, offering practical solutions for improving precision in both fields. His approach—treating mechanical imperfections as estimation problems rather than hardware limitations—marks a notable methodological achievement with potential for broad impact in precision instrumentation and autonomous navigation.
Research Focus
Key Achievements
Top Papers
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