Olga Barinova

Samsung (Russia), Samsung (United States)

Papers

Total Citations

H-Index

About

Olga Barinova is a computer vision and robotics researcher whose work centers on visual simultaneous localization and mapping (SLAM), indoor navigation, and robotic perception. Her research addresses some of the most pressing challenges in enabling autonomous robots to understand and navigate complex indoor environments reliably and accurately. Among her most notable contributions is her 2019 feasibility study on measuring the robustness of Visual SLAM systems, which has garnered 58 citations and provides a rigorous evaluation of RGB-D SLAM methods — including the widely used ORB-SLAM2 — for real-world indoor robot navigation. This work has become a valuable reference point for researchers assessing the practical limits of state-of-the-art localization systems. Complementing this, Barinova co-developed DISCOMAN, a large-scale synthetic dataset comprising 200 long sequences designed specifically for training and benchmarking semantic SLAM algorithms. By simulating realistic home robot trajectories across diverse indoor layouts, DISCOMAN offers the research community a much-needed resource for advancing odometry, mapping, and navigation techniques. Barinova's contributions reflect a rigorous, benchmark-driven approach to robotics research, helping bridge the gap between algorithmic development and real-world deployment — making her work particularly relevant to students and engineers working at the intersection of computer vision and autonomous systems.