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About

Andrei Potapov is a mobile robotics researcher whose work focuses on the intersection of neural fields, motion planning, and visual SLAM. His key contributions address fundamental challenges in autonomous navigation: computational efficiency, memory scalability, and trajectory optimality. His most influential work, "NFOMP" (2022, 23 citations), introduces a neural field-based optimal motion planner for differential drive robots, overcoming the limitations of classical sampling-based methods by producing smooth, short trajectories in reasonable computation time. In "MeSLAM" (2022, 23 citations), Potapov tackles the scalability bottleneck in long-term SLAM by leveraging neural fields to create memory-efficient representations, reducing the computational resources required for localization and planning. His "MuCaSLAM" (2022, 11 citations) further improves SLAM robustness by implementing a CNN-based frame quality assessment layer for omnidirectional cameras, enabling efficient operation on resource-constrained platforms. Most recently, "LocoNeRF" (2023) extends his work to precise visual localization using NeRF-based local structure from motion. Potapov’s research consistently demonstrates how neural representations can make autonomous systems more practical and efficient, with his papers collectively accumulating over 60 citations and establishing him as a rising contributor to neural field applications in robotics.