Artem Nenashev
Papers
2
Total Citations
7
H-Index
2
About
Artem Nenashev is a robotics researcher whose work lies at the intersection of autonomous navigation and computer vision, with a focus on enabling safe, efficient movement in complex environments. His key contributions include the development of DNFOMP (Dynamic Neural Field Optimal Motion Planner), a novel framework that addresses the critical challenge of motion planning for autonomous robots in cluttered, dynamically changing settings. This work, published in 2023, integrates neural field representations with optimal planning to balance safety, comfort, and speed constraints—a significant step forward for autonomous driving. Nenashev has also advanced visual localization with LocoNeRF, a NeRF-based approach that enhances Structure from Motion (SfM) techniques for precise robot positioning. By leveraging neural radiance fields, this method improves localization accuracy in challenging scenes, offering a robust alternative to traditional geometric methods. While his publications are recent, their early citations (4 and 3, respectively) signal growing interest from the robotics community. Nenashev’s research is particularly notable for bridging deep learning and classical planning, making him a promising voice in next-generation autonomous systems.
Research Focus
Key Achievements
Top Papers
- 1
- 2