Papers
1
Total Citations
16
H-Index
1
About
Canben Yin is a researcher whose work lies at the intersection of computer vision, robotics, and 3D perception, with a particular focus on enabling robust visual simultaneous localization and mapping (SLAM) in dynamic environments. His most cited paper, "Removing dynamic 3D objects from point clouds of a moving RGB-D camera" (2015, 16 citations), addresses a critical challenge in SLAM systems: the presence of moving objects that degrade the accuracy of visual odometry and loop-closure detection. Yin proposed a method to filter out dynamic 3D objects from point clouds captured by a moving RGB-D camera, significantly improving the reliability of SLAM in real-world, non-static scenes. This contribution is foundational for applications in autonomous navigation, augmented reality, and robotics, where environments are rarely perfectly static. While his citation count reflects a focused, early-career impact, his work demonstrates a clear understanding of practical limitations in state-of-the-art SLAM and offers a targeted solution. Yin’s research is especially valuable for students and engineers developing robust perception systems that must operate in cluttered, dynamic spaces.
Research Focus
Key Achievements
Top Papers
- 1Removing dynamic 3D objects from point clouds of a moving RGB-D camera16 citations · 2015