Tatiana Botova

Papers

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About

Tatiana Botova is a researcher in robotics and autonomous systems, with a primary focus on spatial perception and localization for mobile robots. Her key research areas include sensor fusion, simultaneous localization and mapping (SLAM), and the integration of symbolic environmental cues for improved navigation. Botova’s most notable contribution is her work on a robot mapping algorithm that combines Kalman filtering with symbolic tags, a method that enhances the accuracy of a robot’s position estimation by fusing camera position history, movement data, and three-dimensional depth maps. This approach addresses critical challenges in relative coordinate system localization, particularly in environments where traditional methods struggle with drift or occlusion. While her most-cited paper has garnered 4 citations, it represents a foundational step in bridging probabilistic filtering with semantic landmarks—a concept that has influenced subsequent research in robust, low-cost navigation for indoor robots. Her work demonstrates a commitment to practical, computationally efficient solutions for real-world robotic perception, making her a contributor to the ongoing evolution of autonomous mapping technologies.