Jiming Chen

Papers

Total Citations

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About

Jiming Chen is an emerging researcher specializing in robotic perception, autonomous navigation, and multi-sensor fusion systems. His work addresses fundamental challenges in enabling robots and autonomous vehicles to operate reliably across complex, real-world environments where individual sensors frequently fail or underperform. Chen's most notable contribution, AF-RLIO, introduces an adaptive fusion framework combining radar, LiDAR, and inertial measurement data to achieve robust odometry in demanding conditions such as smoke-filled environments, tunnels, and adverse weather. This work directly tackles the brittleness of single-sensor navigation pipelines, representing a meaningful step forward in all-weather autonomous systems. Complementing this, his research on PB-MOT advances 3D multi-object tracking by incorporating pose-aware association strategies that overcome the limitations of traditional geometry-based metrics, particularly at long ranges — a critical requirement for safe autonomous driving. Though Chen's published work is recent, with his 2025 papers already accumulating early citations, his research targets high-impact problems at the intersection of sensor fusion, state estimation, and perception. Students interested in autonomous systems, LiDAR-radar integration, or real-time 3D tracking will find his contributions a valuable foundation for exploring robust robotic intelligence in unstructured environments.