Shuolong Chen
Papers
7
Total Citations
56
H-Index
4
About
Shuolong Chen is an emerging robotics researcher whose work centers on sensor fusion, spatiotemporal calibration, and continuous-time optimization for autonomous systems. His research addresses one of the most fundamental challenges in modern robotics: accurately calibrating the spatial and temporal relationships between heterogeneous sensor suites — including LiDARs, IMUs, cameras, radar, and event cameras — that autonomous vehicles and mobile robots depend upon for reliable perception and navigation. Chen's most significant contribution is the development of targetless calibration frameworks that eliminate the need for specialized calibration targets, dramatically improving practicality in real-world deployments. His iKalibr system (2025, 17 citations) represents a unified approach to calibrating diverse integrated inertial systems, while his multi-LiDAR multi-IMU calibration work (2024, 19 citations) tackles the growing complexity of multi-sensor platforms. Collectively, his papers have accumulated over 50 citations within a remarkably short publication window, signaling strong community interest. Particularly noteworthy is his expanding scope into novel sensor modalities, including event cameras (eKalibr-Stereo) and radar-inertial systems (River), positioning him at the frontier of next-generation robotic perception. His open-source contributions further amplify his impact, making sophisticated calibration tools accessible to the broader research community.
Research Focus
Key Achievements
Top Papers
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