Mikhail Chekanov
Papers
1
Total Citations
2
H-Index
1
About
Mikhail Chekanov is a researcher whose work lies at the intersection of computational imaging, inverse problems, and precision metrology, with a particular focus on computed tomography (CT) and simultaneous localization and mapping (SLAM) techniques. His most notable contribution is the development of "TomoSLAM," a novel framework that applies factor graph optimization to refine rotation angle measurements in microtomography. This work addresses a critical challenge in CT imaging: mechanical backlashes and sensor inaccuracies in rotation stages can introduce significant artifacts, degrading image quality. By treating the unknown rotation angles as variables to be optimized—much like a robot localizing itself in an unknown environment—Chekanov’s approach enables high-fidelity reconstruction without requiring perfectly calibrated hardware. Though early in its citation impact (2 citations as of the paper’s 2022 publication), the work has been recognized for its conceptual originality, bridging robotics and tomography. Chekanov’s research is particularly valuable for applications in materials science and biomedical imaging, where sub-micron precision is essential. His work exemplifies a growing trend of using probabilistic graphical models to correct systematic errors in imaging pipelines, offering a path toward more robust and accessible micro-CT systems.
Research Focus
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Top Papers
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