Pengwei Zang
Papers
1
Total Citations
17
H-Index
1
About
Pengwei Zang is a researcher whose work lies at the intersection of robotics, autonomous driving, and multi-modal sensor fusion. His primary research focuses on the critical challenge of calibrating heterogeneous sensors—particularly LIDAR and cameras—to enable robust perception in complex environments. Zang’s most notable contribution is the development of **SST-Calib**, a novel framework for simultaneous spatial-temporal parameter calibration between LIDAR and camera systems. This work, published in 2022 and already garnering 17 citations, addresses a fundamental bottleneck in sensor fusion: ensuring that depth-rich LIDAR data and semantically dense camera imagery are precisely aligned in both space and time. By solving this calibration problem, Zang’s research directly enables more reliable object detection and scene understanding for autonomous vehicles operating in dynamic, cluttered driving scenarios. His contributions are particularly impactful given the growing reliance on multi-modal perception in robotics, where even minor misalignments can compromise safety and performance. For students and researchers exploring sensor fusion, Zang’s work offers a practical, data-driven pathway to achieving the high-fidelity integration necessary for next-generation autonomous systems.
Research Focus
Key Achievements
Top Papers
- 1