Alena Savinykh
Papers
5
Total Citations
68
H-Index
5
About
Alena Savinykh is a leading researcher in mobile robotics, specializing in optimal motion planning, simultaneous localization and mapping (SLAM), and neural field-based perception. Her work addresses critical challenges in autonomous navigation, particularly for robots operating under nonholonomic constraints and limited computational resources. Her most cited paper, "NFOMP: Neural Field for Optimal Motion Planner of Differential Drive Robots With Nonholonomic Constraints" (2022, 23 citations), introduces a neural field approach that outperforms classical sampling-based methods by generating smooth, short trajectories efficiently. Savinykh also developed "MeSLAM: Memory Efficient SLAM based on Neural Fields" (2022, 23 citations), which tackles scalability issues in long-term robot operation by reducing map size and computational demands. Her innovative "MuCaSLAM" (2022, 11 citations) uses CNN-based frame quality assessment to enhance visual SLAM robustness on multi-camera robots with limited power. Notably, she was part of the NimbRo team that won the RoboCup@Home 2024 Open Platform League, showcasing her work on anthropomorphic service robots integrating foundation models for perception and planning. With over 68 citations across her key papers, Savinykh’s contributions are advancing efficient, real-world autonomous navigation.
Research Focus
Key Achievements
Top Papers
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- 2MeSLAM: Memory Efficient SLAM based on Neural Fields23 citations · 2022
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