Artificial Intelligence Research Lab
Mission – Solve Intelligence
The AI Lab is a fundamental research lab focusing on developmental robotics, autonomous learning and language acquisition. We are a team of professor and research students who develop algorithmic, mathematical, statistical, and computational methods that are central in the knowledge engineering of Artificial intelligence.
Our mission is to conduct cutting-edge research on artificial intelligence and to transfer the latest technologies to our products for creating a more positive impact on the world.
Areas of interest include, but are not limited to:
We are aiming to implementing robot navigation and localization so that it can move around the world autonomously. We perform SLAM to build a map in any given indoor environment. Using probability theory, robot can locate its location mainly using laser range finder without GPS and move to the target autonomously in this pre-SLAMed environment with errors within 5 cm. Robot has high level user-defined motion planning and recovering strategy to optimize the navigating path and avoid dynamic obstacles around it. Also the robot can controlled using the customized voice commands with speech recognition and NLP technologies.
Real-Time Object Detection with deep learning
It is aiming to find and classify a variable number of objects in images using deep learning. The task is assigning a label and a bounding box to all objects in the image. It is a simpler structure of network with real-time property(150fps), which is able to process streaming video in real-time with less than 25 milliseconds of latency.
- Planning Paths with Fewer Turns on Grid Maps
- RGB-D image-based pose estimation with Monte Carlo localization
- High-Speed and Accurate Laser Scan Matching Using Classified Features
- Tao Hu, May Huang, A New Stereo Matching Algorithm for Binocular Vision, ICHIT 2009, Korea
- Peng Wang, Yi Liu, Taiheng (Matthew) Jin, May Huang, Real-Time Map Generation Using Constraint Delaunay Triangulation ICHIT 2011, Korea
- Xiaolan Bai, May Huang,Accuracy Analysis of Power Characterization and Modeling ICHIT 2011, Korea