Anastasiia Sochenkova

Peoples' Friendship University of Russia

Papers

Total Citations

H-Index

About

Anastasiia Sochenkova is a researcher in robotics and autonomous systems, with a primary focus on sensor fusion, spatial mapping, and localization algorithms. Her most cited work, "Robot mapping algorithm based on Kalman filtering and symbolic tags" (2017), introduces a novel method for determining a robot’s position within a relative coordinate system. By integrating a history of camera positions, robot movement data, symbolic tags, and three-dimensional depth maps—while accounting for superimposition accuracy—she advances the robustness of real-time mapping. This contribution is particularly valuable for applications in indoor navigation and autonomous exploration, where precise localization is critical. Although her citation count is modest, her work represents a foundational step in combining Kalman filtering with symbolic environmental cues, a niche that bridges classical estimation theory and modern semantic mapping. Sochenkova’s research contributes to the broader goal of enabling robots to operate reliably in unstructured environments, making her a notable voice in the development of practical, cost-effective mapping solutions for mobile robotics.