Papers
4
Total Citations
403
H-Index
3
About
Jie Pan is a robotics and autonomous systems researcher whose work centers on state estimation, multi-sensor fusion, and sensor calibration — foundational challenges in building reliable autonomous robots. His most influential contribution, "A General Optimization-based Framework for Local Odometry Estimation with Multiple Sensors" (2019, 296 citations), established a versatile framework enabling robots to integrate diverse sensor combinations — including cameras, IMUs, and LiDAR — to achieve robust and accurate localization. This work has become a widely referenced resource in the mobile robotics and autonomous vehicle communities. Complementing this, his 2020 paper on temporal and rotational calibration of heterogeneous sensors (65 citations) addressed the often-overlooked challenge of synchronizing fundamentally different sensor modalities, a critical step for reliable multi-sensor systems. Pan extended his foundational framework to tackle global drift-free pose estimation in a 2025 follow-up study, demonstrating a sustained commitment to advancing the field. His applied research also spans industrial robotics, including object detection and tracking for drilling robots in challenging underground coal mine environments. With nearly 400 cumulative citations across his core works, Pan's research has meaningfully shaped how autonomous systems perceive and navigate the world.
Research Focus
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