Thrilochan Sharma
Papers
1
Total Citations
24
H-Index
1
About
Thrilochan Sharma is a leading researcher in autonomous navigation and mobile robotics, with a primary focus on Simultaneous Localization and Mapping (SLAM) algorithms. His most-cited work, a comparative analysis of ROS-based 2D and 3D SLAM algorithms for Autonomous Ground Vehicles (2020, 24 citations), provides a critical benchmark for selecting optimal SLAM techniques in real-world autonomous driving scenarios. By systematically evaluating the performance of different SLAM implementations, Sharma has helped bridge the gap between theoretical algorithm development and practical deployment in unknown environments. His research directly addresses the core challenge of enabling robotic vehicles to move autonomously by accurately estimating sensor motion and reconstructing environmental structures. Sharma’s contributions are particularly valuable for engineers and researchers working on self-driving cars, warehouse robots, and field robotics, where reliable SLAM is essential. With his work gaining increasing recognition in the autonomous systems community, Thrilochan Sharma continues to advance the state of the art in perception and navigation for intelligent ground vehicles.
Research Focus
Key Achievements
Top Papers
- 1