Egor Pristanskiy
Papers
1
Total Citations
84
H-Index
1
About
Egor Pristanskiy is a leading researcher at the intersection of robotics, computer vision, and autonomous systems, with a primary focus on intelligent warehouse automation and UAV-based inventory management. His most influential work, "WareVision: CNN Barcode Detection-Based UAV Trajectory Optimization for Autonomous Warehouse Stocktaking" (2020), has garnered 84 citations, establishing a foundational approach for real-time barcode scanning using Convolutional Neural Networks aboard heterogeneous UAV platforms. Pristanskiy’s key contribution lies in optimizing UAV trajectories by leveraging scanned barcodes as spatial landmarks, enabling robust localization and navigation in challenging, low-light warehouse environments. This innovation directly addresses critical bottlenecks in logistics—improving stocktaking accuracy and efficiency while reducing human labor. Beyond this flagship paper, his research spans deep learning for object detection, sensor fusion, and autonomous robotic coordination. Pristanskiy’s work has been recognized for its practical impact, bridging theoretical advances in CNN-based perception with deployable solutions for industry. His achievements demonstrate a rare ability to translate complex computer vision algorithms into real-world robotic systems, making him a notable figure in the growing field of autonomous logistics and smart warehousing.
Research Focus
Key Achievements
Top Papers
- 1