Shibin Song

Shandong University of Science and Technology

Papers

Total Citations

H-Index

About

Shibin Song is a researcher focused on advancing autonomous mobile robotics and 3D computer vision, with key contributions in path planning and point cloud processing. Their most cited work, "Improved RRT path planning algorithm based on growth evaluation" (2021), addresses critical limitations in the Rapidly-exploring Random Tree (RRT) algorithm—specifically high time consumption, excessive sampling, and low operational efficiency—by introducing a growth evaluation mechanism that optimizes path generation for autonomous robots. This work has garnered 3 citations and represents a foundational improvement in robotic navigation. More recently, Song has tackled the challenging problem of dense point cloud registration in "Point Cloud Registration Based on Multiple Neighborhood Feature Difference" (2025), which enhances accuracy in applications like robotic navigation, autonomous driving, and 3D measurement by leveraging multiple neighborhood feature differences to overcome high computational costs and registration errors. With a total of 4 citations across their published works, Song’s research demonstrates a clear trajectory from improving fundamental robotic algorithms to addressing complex 3D perception challenges, positioning their work as valuable for students and researchers in robotics and computer vision seeking efficient, practical solutions for real-world autonomous systems.