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About

Chenghao Shi is a robotics and autonomous systems researcher whose work sits at the intersection of 3D perception, simultaneous localization and mapping (SLAM), and deep learning for mobile robots and autonomous driving. His research has made meaningful contributions across several interconnected domains, including semantic scene understanding, sensor fusion, and point cloud processing. Shi's early work on semantic RGB-D SLAM for rescue robots (2020, 30 citations) demonstrated how geometric mapping could be enriched with point-wise semantic labels, advancing robot situational awareness in challenging environments. His 2019 paper on extrinsic calibration and odometry for camera-LiDAR systems (22 citations) addressed a foundational challenge in multi-sensor robotics platforms. More recently, his RDMNet framework (2023, 27 citations) tackled reliable point cloud registration for autonomous driving, improving upon coarse-to-fine correspondence methods. His work on diffusion-based radar point cloud super-resolution (2024, 14 citations) explores all-weather perception using mmWave radar, while SegNet4D (2025, 12 citations) delivers efficient 4D LiDAR semantic segmentation for dynamic scene understanding. With over 110 cumulative citations, Shi's growing body of work reflects a consistent focus on making robotic perception more robust, semantically rich, and computationally practical.