Samuel Kounev
Papers
3
Total Citations
57
H-Index
2
About
Samuel Kounev is a robotics researcher specializing in autonomous navigation, perception, and sensor fusion for mobile robots operating in complex environments. His work centers on developing robust localization and people-detection systems that integrate depth sensing, convolutional neural networks, and multi-sensor data—including wheel odometry and IMU—to enhance robot performance in real-world settings. Kounev’s most cited paper, "People Detection with Depth Silhouettes and Convolutional Neural Networks on a Mobile Robot" (2021, 48 citations), demonstrates a novel approach to human-robot interaction by combining depth imagery with deep learning for reliable detection on moving platforms. He also contributed a comprehensive survey and experimental comparison of RGB-D indoor navigation methods supported by ROS (2020), offering valuable insights into sensor fusion strategies. Notably, his benchmark for mobile robot localization in industrial environments (2021) addresses the critical need for realistic performance evaluation under challenging conditions, such as poor lighting or sensor noise. With a focus on practical, deployable solutions, Kounev’s work bridges the gap between theoretical algorithms and real-world robotic applications, making him a key figure in advancing autonomous systems for industrial and service robotics.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3