Pengpeng Su

Harbin Institute of Technology, Northeastern University

Papers

Total Citations

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About

Pengpeng Su is a researcher whose work lies at the intersection of low-cost sensing, 3D perception, and autonomous navigation. His primary research areas include LiDAR sensor design, simultaneous localization and mapping (SLAM), and place recognition for mobile robotics. Su’s major contributions are twofold: he has developed affordable, high-performance LiDAR systems and advanced the semantic understanding of 3D point clouds. His most cited work, “3-D Dense Rangefinder Sensor With a Low-Cost Scanning Mechanism” (2020, 13 citations), addresses the prohibitive cost of high-resolution LiDAR by proposing a novel scanning mechanism that improves vertical resolution without expensive multi-channel arrays. In “Semantic Scan Context: Global Semantic Descriptor for LiDAR-based Place Recognition” (2021, 9 citations), Su introduced a method that fuses geometric and semantic features to create view-invariant descriptors for robust loop closure detection in SLAM—a critical capability for long-term autonomous operation. His earlier work, “Design and implementation of LiDAR navigation system based on triangulation measurement” (2017, 7 citations), laid the groundwork for compact, accurate navigation systems. Collectively, Su’s research demonstrates a clear trajectory from hardware innovation to intelligent perception, making autonomous systems more accessible and reliable.