Papers
2
Total Citations
52
H-Index
2
About
Jiaquan Yan is a leading researcher in robotics and autonomous systems, with a primary focus on multi-sensor fusion and simultaneous localization and mapping (SLAM). His most significant contribution is the development of **Lvio-Fusion**, a self-adaptive, tightly coupled SLAM framework that fuses stereo camera, IMU, and LiDAR data using an actor-critic reinforcement learning method. This work addresses a critical challenge in mobile robotics: the variable performance of sensors across different environments. By intelligently weighting sensor inputs, Lvio-Fusion enables robust state estimation where traditional methods fail. The core paper has garnered **48 citations**, reflecting its impact on the field of autonomous navigation. Yan’s research is particularly notable for bridging classical estimation theory with modern reinforcement learning, creating systems that adapt in real time. His work is essential reading for engineers and researchers developing resilient perception systems for drones, ground robots, and autonomous vehicles operating in complex, unstructured environments.
Research Focus
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