Huan Lei

Guangdong Institute of Intelligent Manufacturing, Guangdong Academy of Sciences

Papers

Total Citations

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About

Huan Lei is a researcher focused on advancing robotics perception and environmental intelligence, with key contributions in simultaneous localization and mapping (SLAM) and deep learning-based object detection. Lei’s most cited work, “Simultaneous Localization and Mapping based on Lidar” (2019), tackles critical challenges in autonomous navigation by proposing an improved Rao-Blackwellised particle filter (RBPF) method for lidar-based SLAM. This approach addresses issues of low accuracy, high particle demands, and particle degradation, offering a more efficient solution for real-time mapping in dynamic environments. Building on this, Lei’s 2022 study, “An application case of object detection model based on Yolov3-SPP model pruning,” applies computer vision to municipal solid waste classification, using model pruning to reduce computational costs while maintaining detection accuracy. This work highlights Lei’s commitment to sustainable technology, aiming to lower labor risks and environmental harm in waste management. With a growing citation impact, Lei’s research bridges theoretical advances in robotic perception and practical applications in environmental sustainability, demonstrating a clear trajectory toward smarter, safer autonomous systems.