Dongdong Yang

Shanghai Eye Disease Prevention & Treatment Center

Papers

Total Citations

H-Index

About

Dongdong Yang is a researcher specializing in robotics perception, 3D mapping, and simultaneous localization and mapping (SLAM), with a particular focus on dynamic environment handling. His major contribution lies in developing efficient methods for rejecting dynamic obstacles during 3D map updating—a critical challenge in real-world autonomous navigation. His most cited work, "Dynamic Obstacles Rejection for 3D Map Simultaneous Updating" (2018, 14 citations), introduces a novel approach that eliminates spurious trails left by moving objects in point cloud maps. By leveraging view frustum filters and bidirectional KD-tree searching within view overlaps, Yang’s method achieves high efficiency while maintaining map accuracy. This work directly addresses the persistent problem of map corruption in dynamic scenes, making it valuable for applications in autonomous driving, robotics, and augmented reality. Yang’s research bridges the gap between theoretical SLAM algorithms and practical deployment in cluttered environments, demonstrating a clear impact on the field despite the relatively early stage of citation accumulation. His contributions are particularly notable for their simplicity and effectiveness, offering a scalable solution for real-time 3D mapping systems.