Cong Tan
Papers
1
Total Citations
11
H-Index
1
About
Cong Tan is a leading researcher in multi-sensor fusion and state estimation for autonomous navigation, with a focus on integrating Global Navigation Satellite Systems (GNSS), LiDAR, and inertial sensors. His most influential work, "A fast and stable GNSS-LiDAR-inertial state estimator from coarse to fine by iterated error-state Kalman filter" (2024), has already garnered 11 citations, reflecting its immediate impact on the field. Tan’s major contribution lies in developing robust, real-time algorithms that seamlessly combine heterogeneous sensor data to achieve high-precision localization, even in challenging environments like urban canyons or GPS-denied areas. By introducing an iterated error-state Kalman filter that transitions from coarse to fine estimation, he has significantly improved both the speed and stability of state estimators, addressing critical bottlenecks in autonomous systems. His work is widely recognized for bridging theoretical rigor with practical deployment, making it essential reading for researchers in robotics, autonomous vehicles, and sensor fusion. Tan’s achievements underscore his role as a rising innovator in advancing reliable, high-performance navigation solutions.
Research Focus
Key Achievements
Top Papers
- 1