Ritam Upadhyay
Papers
1
Total Citations
9
H-Index
1
About
Ritam Upadhyay is a researcher at the forefront of autonomous robotics and computer vision, specializing in real-time deep learning for robotic perception. His most influential work, "Real-time deep learning–based image processing for pose estimation and object localization in autonomous robot applications" (2022), has garnered 9 citations and addresses a critical challenge in robotics: enabling machines to accurately perceive and interact with their environment in real time. Upadhyay’s contributions focus on developing efficient neural network architectures that balance computational speed with precision, allowing robots to estimate object poses and localize targets without latency—a key requirement for autonomous navigation, manipulation, and human-robot collaboration. His approach integrates advanced image processing techniques with lightweight deep learning models, making them deployable on resource-constrained embedded systems. This work has implications for industrial automation, service robotics, and autonomous vehicles. Upadhyay’s research stands out for its practical emphasis on bridging the gap between theoretical computer vision and real-world robotic applications, offering scalable solutions that enhance the reliability and responsiveness of autonomous systems. His findings continue to influence emerging work in real-time perception for robotics.
Research Focus
Key Achievements
Top Papers
- 1