Minshun Wu
Papers
1
Total Citations
15
H-Index
1
About
Minshun Wu is a researcher at the forefront of computer vision and robotics, with a primary focus on simultaneous localization and mapping (SLAM) for augmented reality and autonomous systems. His most notable work, "SDF-SLAM: A Deep Learning Based Highly Accurate SLAM Using Monocular Camera Aiming at Indoor Map Reconstruction With Semantic and Depth Fusion" (2022), has garnered 15 citations for its innovative integration of deep learning with monocular SLAM. Wu's key contribution lies in fusing semantic understanding with depth estimation, enabling more robust and accurate 3D map reconstruction from a single camera—a critical advancement for indoor navigation in AR and unmanned driving. By leveraging neural networks to extract environmental cues, his approach overcomes traditional monocular SLAM limitations, such as scale ambiguity and drift. This work demonstrates Wu's ability to bridge theoretical deep learning techniques with practical robotic perception challenges. His research continues to push the boundaries of how machines interpret and navigate complex indoor spaces, making him a rising voice in the SLAM community.
Research Focus
Key Achievements
Top Papers
- 1