Mark Griguletskii

About

Mark Griguletskii is a researcher at the intersection of robotics, computer vision, and computational imaging, with a primary focus on Simultaneous Localization and Mapping (SLAM) and robot localization. His most impactful work, the 2023 paper "Comparison of modern open-source Visual SLAM approaches," has garnered 46 citations, establishing him as a key voice in benchmarking the rapidly evolving landscape of visual SLAM—a fundamental challenge in enabling autonomous navigation. Griguletskii’s contributions extend beyond comparative analysis; he developed the Prior Distribution Refinement method for generating accurate reference trajectories, a crucial tool for fairly evaluating robot positioning algorithms. In a novel interdisciplinary application, his work "TomoSLAM" adapts factor graph optimization—a core SLAM technique—to refine rotation angles in microtomography, addressing mechanical inaccuracies in computed tomography systems. This creative transfer of robotics methodologies to medical imaging highlights his versatility. By providing open-source benchmarks and novel estimation techniques, Griguletskii’s research directly supports practitioners in selecting robust SLAM solutions and advancing the precision of autonomous systems.

Research Focus

Key Achievements

Top Papers

1. 1
   Comparison of modern open-source Visual SLAM approaches

   46 citations · 2023
2. 2
   Comparison of modern open-source visual SLAM approaches

   4 citations · 2021
3. 3
   TomoSLAM: factor graph optimization for rotation angle refinement in microtomography

   2 citations · 2022
4. 4
   Prior Distribution Refinement for Reference Trajectory Estimation With the Monte Carlo-Based Localization Algorithm

   2 citations · 2023

Key Collaborators

Contact & Links