R. Jegadeeshwaran
Papers
2
Total Citations
19
H-Index
2
About
R. Jegadeeshwaran is a researcher focused on advancing autonomous navigation and perception systems for mobile robotics and self-driving vehicles. Their key research areas include simultaneous localization and mapping (SLAM), autonomous driving perception, and bridging the synthetic-to-real domain gap in deep learning. A major contribution is their work on implementing the GMapping algorithm for mobile robot SLAM, which addresses the critical challenge of enabling robots to localize themselves while concurrently building maps of unknown environments—a foundational requirement for truly autonomous navigation. This work has garnered 11 citations. More recently, Jegadeeshwaran has tackled the pressing issue of domain adaptation in autonomous driving, proposing a method to estimate the synthetic-to-real gap using feature embedding. This research, cited 8 times, is vital for training robust deep learning models on large synthetic datasets while maintaining performance in real-world conditions, directly impacting tasks like visual odometry and object detection. Through these contributions, Jegadeeshwaran is helping to make autonomous systems more reliable and practical for real-world deployment.
Research Focus
Key Achievements
Top Papers
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