Arun Srivatsan Rangaprasad
Papers
1
Total Citations
21
H-Index
1
About
Arun Srivatsan Rangaprasad is a leading researcher in robotics, with a core focus on pose estimation, sensor-based perception, and geometric uncertainty modeling. His most influential work introduces a novel approach to online pose estimation by leveraging the Bingham distribution, a probabilistic model on the unit sphere that more accurately captures rotational uncertainty than traditional Gaussian methods. This breakthrough, detailed in his 2017 paper with 21 citations, addresses critical limitations in applications such as registration, hand-eye calibration, and SLAM, where precise orientation tracking is essential. Rangaprasad’s contributions extend to developing linear filters that maintain computational efficiency while improving robustness in real-time robotic systems. His work has been recognized for bridging theoretical geometry with practical deployment, earning him a reputation for advancing state estimation in cluttered or ambiguous environments. By rethinking how uncertainty is represented in pose parameters, he has provided the robotics community with tools that enhance both accuracy and reliability, making his research a key reference for students and engineers working on autonomous navigation and manipulation.
Research Focus
Key Achievements
Top Papers
- 1Bingham Distribution-Based Linear Filter for Online Pose Estimation21 citations · 2017