Jian Weng

Papers

Total Citations

142

H-Index

About

Jian Weng is a leading researcher in intelligent robotics and autonomous systems, with a focus on real-time perception and navigation. His work spans semantic segmentation for autonomous driving, localization and mapping for unmanned ground vehicles, and surface recognition for biped robots. Weng’s major contributions include the development of LMFFNet, a lightweight network that achieves fast and accurate semantic segmentation without sacrificing performance—a critical advancement for real-time applications in autonomous driving and robotics. This work has garnered 104 citations, reflecting its significant impact. He also proposed the Heuristic Monte Carlo Algorithm (HMCA), which integrates Monte Carlo localization with the Discrete Hough Transform to enable robust real-time localization and mapping in cluttered indoor environments, a key challenge for autonomous UGV navigation. Additionally, Weng pioneered a force-sensory walking-pattern classification method for biped robots, enabling cost-effective surface recognition to ensure safe locomotion in complex human environments. His research is notable for balancing computational efficiency with high accuracy, making his solutions practical for real-world deployment. Weng’s work continues to influence the fields of autonomous navigation and robotic perception.