Jiapeng Zhong
Papers
2
Total Citations
5
H-Index
2
About
Jiapeng Zhong is a roboticist advancing the frontiers of autonomous perception and long-term visual navigation. His research centers on semantic SLAM (Simultaneous Localization and Mapping), robust data association, and lifelong calibration for stereo vision systems. Zhong’s most notable contribution is the development of **DHDP-SLAM**, a novel framework that leverages a Dynamic Hierarchical Dirichlet Process to solve data association in semantic SLAM. This work, published in 2024, addresses a critical bottleneck in enabling robots to understand and map dynamic environments, earning early recognition with 3 citations. In parallel, his 2021 paper **CalQNet** tackles the practical challenge of detecting calibration quality degradation in life-long stereo camera setups. This is vital for mobile robots that rely on precise extrinsic parameters for visual stereo matching; Zhong’s method allows systems to autonomously monitor and flag when a “factory” calibration has drifted, ensuring sustained accuracy over extended deployments. By bridging probabilistic modeling with real-world robotic reliability, Zhong’s work directly impacts the durability and intelligence of autonomous systems operating in unstructured environments.
Research Focus
Key Achievements
Top Papers
- 1
- 2