Evgeny Kruzhkov
Papers
3
Total Citations
39
H-Index
3
About
Evgeny Kruzhkov is a robotics researcher whose work sits at the intersection of motion planning, visual perception, and autonomous navigation. His primary research areas include optimal motion planning for nonholonomic robots, visual simultaneous localization and mapping (SLAM), and cross-modal localization using deep neural networks. Kruzhkov’s most significant contribution is the development of NFOMP, a neural field-based optimal motion planner that addresses the long-standing trade-off between computational efficiency and trajectory quality for differential drive robots. By leveraging neural representations, his approach produces smooth, short paths far faster than classical sampling-based methods, a breakthrough reflected in the 23 citations of his 2022 paper. In MuCaSLAM, he introduced a CNN-based frame quality assessment layer that dramatically improves the robustness and computational efficiency of omnidirectional visual SLAM on resource-constrained platforms. His more recent work, CloudVision, demonstrates a novel method for 6-DoF visual localization within prebuilt LiDAR point clouds, bridging the gap between camera and LiDAR sensing. With a growing citation footprint and a clear trajectory toward practical, real-time autonomy, Kruzhkov is establishing himself as a rising voice in intelligent mobile robotics.
Research Focus
Key Achievements
Top Papers
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