Aleksej Makarov

Papers

Total Citations

H-Index

About

Aleksej Makarov is a computer vision researcher whose work focuses on enabling real-time depth perception for resource-constrained embedded platforms. His primary research areas include stereo vision, depth processing, and efficient computer vision algorithms for robotics and autonomous systems. Makarov’s major contribution lies in developing passive stereo systems that can achieve accurate depth information without the power-hungry active sensors like LiDAR, making them ideal for low-cost, energy-efficient robotic applications. His most cited work, "Real-time depth processing for embedded platforms" (2017), has garnered 3 citations and addresses the critical challenge of obtaining depth data on embedded systems while maintaining robustness to varying lighting conditions. This research is particularly valuable for mobile robots, drones, and other autonomous platforms where power efficiency and cost are paramount. Makarov’s work helps bridge the gap between high-performance computer vision algorithms and the practical constraints of embedded hardware, advancing the field of accessible, real-time 3D perception for next-generation autonomous systems.