Pingliang Zeng

Papers

Total Citations

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About

Pingliang Zeng is a researcher specializing in robotics and autonomous navigation, with a primary focus on simultaneous localization and mapping (SLAM) for industrial inspection applications. His work centers on developing robust, lightweight algorithms for substation inspection robots, addressing critical challenges in positioning accuracy and environmental perception. Zeng’s most cited paper, “Lidar SLAM Based on Particle Filter and Graph Optimization for Substation Inspection” (2022, 11 citations), introduces a hybrid approach that combines Rao-Blackwellized Particle Filter (RBPF) with graph optimization to overcome the limitations of traditional 2D SLAM in complex substation environments. He further contributes to sensor enhancement with “A Laser Data Compensation Algorithm Based on Indoor Depth Map Enhancement” (2023, 2 citations), which tackles the blind-spot issue of 2D LiDAR by fusing depth data for improved obstacle detection. His third notable work, “NIMLS-ICP: An ICP Variant Suitable for Substation Scenarios” (2023, 2 citations), proposes a tailored iterative closest point algorithm for precise point cloud registration in industrial settings. With a growing citation record, Zeng’s research is directly applicable to smart grid maintenance and autonomous robotics, offering practical solutions for real-world deployment. His contributions are particularly valuable for students and engineers working on SLAM, sensor fusion, and field robotics.