Filipp Konokhov

Papers

Total Citations

H-Index

About

Filipp Konokhov is a researcher in robotics and computer vision, with a primary focus on semantic SLAM (Simultaneous Localization and Mapping) for indoor environments. His most notable contribution is the creation of DISCOMAN (Dataset of Indoor SCenes for Odometry, Mapping And Navigation), a large-scale synthetic dataset designed to train and benchmark semantic SLAM algorithms. This dataset, featuring 200 long sequences with 3,000–5,000 frames each, was generated using realistic home layouts and robot-like trajectories, addressing a critical gap in available training data for autonomous navigation systems. While his citation count is still growing—with 3 citations to date for this foundational work—the dataset represents a significant step toward enabling robots to understand and map complex indoor spaces semantically. Konokhov’s work is particularly valuable for researchers developing home robots that need to navigate dynamic environments, as DISCOMAN provides a controlled yet realistic testbed for algorithm development. His contributions highlight the importance of high-quality synthetic data in advancing embodied AI and autonomous navigation.