Long Gao

Papers

Total Citations

H-Index

About

Long Gao is a researcher specializing in robotic perception and autonomous systems, with a core focus on LiDAR depth completion and robotic manipulator kinematics. His most cited work introduces DAN‐Conv, a depth-aware non-local convolution method that addresses the critical challenge of generating dense depth predictions from sparse LiDAR data—a task essential for safe navigation in autonomous driving and robotics. By integrating RGB image guidance with sparse depth maps, this approach significantly improves accuracy in complex environments, earning 6 citations and establishing a foundation for subsequent sensor fusion research. Earlier, Gao contributed to the simulation and kinematic analysis of the PUMA 560 manipulator using MATLAB, providing a detailed framework for forward and inverse kinematics, as well as trajectory planning, which remains a reference for educational and prototyping purposes. His work bridges practical robotic control with advanced deep learning for perception, demonstrating both theoretical rigor and applied impact. With a growing citation record, Gao’s contributions are shaping how robots understand and interact with their surroundings, making him a promising voice in the field of intelligent robotics.