Xiuwan Chen
Papers
1
Total Citations
7
H-Index
1
About
Xiuwan Chen is a leading researcher in mobile robotics and autonomous navigation, with a primary focus on real-time dense visual-inertial simultaneous localization and mapping (SLAM). Their most influential work, "DiT-SLAM: Real-Time Dense Visual-Inertial SLAM with Implicit Depth Representation and Tightly-Coupled Graph Optimization," introduces a groundbreaking approach that leverages implicit depth representations from deep neural networks to generate rich, continuous dense maps in real-time—a significant advance over traditional sparse mapping methods. This work, which has garnered 7 citations since its 2022 publication, addresses a critical challenge in the robotics community by enabling more informative spatial perception for autonomous systems. Chen’s contributions are particularly notable for tightly coupling graph optimization with learned depth codes, achieving both accuracy and computational efficiency. Their research bridges the gap between deep learning and classical SLAM, offering practical solutions for robots operating in complex, unstructured environments. As a rising scholar, Chen’s work is poised to influence next-generation autonomous navigation, with potential applications in service robotics, autonomous driving, and augmented reality.
Research Focus
Key Achievements
Top Papers
- 1