Papers
1
Total Citations
6
H-Index
1
About
Minh Ly is a robotics researcher whose work focuses on advancing visual odometry and motion estimation for mobile robots. His most notable contribution is the development of a robust ego-motion estimation method using ceiling vision, detailed in his highly cited 2012 paper. In this work, Ly introduced a novel approach that leverages a 3D Kinect camera and Speeded-Up Robust Features (SURF) to extract and match features from ceiling images, combined with RANSAC-based outlier rejection for reliable motion recovery. This technique proved particularly valuable for indoor robot navigation, where traditional ground-based visual odometry often fails due to dynamic obstacles or feature-poor floors. With 6 citations, this paper has influenced subsequent research in ceiling-based localization and low-cost visual SLAM systems. Ly’s work demonstrates a practical, computationally efficient solution for mobile robot localization in structured indoor environments, making him a recognized contributor to the field of visual odometry and autonomous navigation.
Research Focus
Key Achievements
Top Papers
- 1Mobile robot ego motion estimation using RANSAC-based ceiling vision6 citations · 2012