Shenghao Li

East China University of Science and Technology, Shanghai Jiao Tong University, Xi'an Jiaotong University

Papers

Total Citations

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About

Shenghao Li is a robotics researcher whose work centers on autonomous navigation, visual perception, and robotic manipulation in complex, unstructured environments. His major contributions lie in developing algorithms that enable mobile robots to operate without prior environmental knowledge. His most cited work, “Autonomous Exploration and Map Construction of a Mobile Robot Based on the TGHM Algorithm” (28 citations), addresses the critical challenge of large-scale autonomous exploration, proposing a method that eliminates the need for manual guidance or a priori maps. Li further advanced real-time localization with “Quantized Self-Supervised Local Feature for Real-Time Robot Indirect VSLAM” (18 citations), tackling the persistent issues of feature drift and mismatches in visual SLAM under varying conditions. More recently, his research has extended to robotic grasping, as seen in “Instance Segmentation of Point Cloud Based on Improved DGCNN for Robotic Grasping” (4 citations), which improves point cloud segmentation accuracy for multi-object stacked scenes. Collectively, Li’s work demonstrates a clear trajectory from foundational mapping and localization to high-level perception for manipulation, making significant strides in enabling robots to perceive, navigate, and interact with their surroundings autonomously.