Maxim Abramov

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About

Maxim Abramov is a robotics researcher specializing in mobile robot localization, sensor fusion, and indoor positioning systems. His work focuses on developing precise and reliable methods for estimating a robot's pose within structured environments. Abramov's most cited paper, "Edge detection based mobile robot indoor localization" (2019, 5 citations), introduces a visual edge detection approach that integrates onboard motion sensors—such as wheel speed and yaw rate sensors—with building schematic plans to achieve accurate indoor pose estimation. This work addresses the critical challenge of maintaining localization accuracy in GPS-denied environments. In his more recent contribution, "Prior Distribution Refinement for Reference Trajectory Estimation With the Monte Carlo-Based Localization Algorithm" (2023, 2 citations), Abramov proposes a novel method for generating reference trajectories, enabling fair and rigorous comparison of localization algorithms. This methodological advancement is essential for benchmarking and validating robot positioning systems. Through his research, Abramov contributes to the foundational tools and techniques that advance autonomous navigation in indoor settings, supporting applications from service robotics to industrial automation.