HUANG Rui 黄睿
Ph.D candidate
Simon Fraser University
British Columbia
Canada

Address:
GrUVi Lab, TASC Building 8004
School of Computing Science,
Simon Fraser University
8888 University Drive
Burnaby, B.C. V5A 1S6, Canada

Email:
rha55@sfu.ca



 

Short Bio
HUANG Rui is a Ph.D candidate supervised by Prof TAN Ping at the School of Computing Science, Simon Fraser University, Canada. Before transferring to the GrUVi lab at SFU to continue his Ph.D research, he was with the Department of Electrical and Computer Engineering, National University of Singapore. He obtained his Bachelor degree in Electronic and Information Engineering with the First-Class Honors from the Hong Kong Polytechnic University. His research interests include computer vision and autonomous unmanned aerial vehicles.
Education
  • 2014 ~ Current. Ph.D Candidate (continuing from NUS). Simon Fraser University, Canada.
  • 2011 ~ 2014. Ph.D Candidate. National University of Singapore, Singapore.
  • 2007 ~ 2011. B.Eng. Hong Kong Polytechnic University, Hong Kong.
Project I: Micro Aerial Vehicle - KayLion

Abstract:

We propose an approach to autonomously control a quadrotor micro aerial vehicle (MAV). With take-off weight of 50 g and 8-min flight endurance, the MAV platform code-named ‘KayLion’ developed is able to perform autonomous flight with pre-planned path tracking. The vision-based autonomous control is realized with a light weight camera system and an ultrasonic range finder integrated to the MAV. An optical flow algorithm is adopted and processed on ground control station to provide position and velocity estimation of the MAV. A model-based position controller is implemented to realize the autonomous flight.

Video Demo:

Project II:Vision based Autonomous Navigation of AR.Drone
Project Page with Code

Abstract:

We present a monocular vision-based autonomous navigation system for a commercial quadcoptor. The quadcoptor communicates with a ground-based laptop via wireless connection. The video stream of the front camera on the drone and the navigation data measured on-board are sent to the ground station and then processed by a novel vision-based SLAM system. In order to handle motion blur and frame lost in the received video, our SLAM system consists of a relocalisation module which achieves fast recovery from tracking failure. An Extended Kalman filter (EKF) is designed for sensor fusion. Thanks to the proposed EKF, accurate 3D positions and velocities can be estimated as well as the scaling factor of the monocular SLAM. Using a motion capture system with millimeter-level precision, we also identify the system models of the quadcoptor and design the PID controller accordingly. We demonstrate that the quadcoptor can navigate along pre-defined paths in an unknown indoor environment with our system using its front camera and onboard sensors only after some simple manual initialization procedures.

Video Demo: Square Path Following

Project III: Collaborative Visual SLAM for Dynamic Target Following
Project Page with Code

Abstract:

Towards autonomous 3D modelling of moving targets, we present a system where multiple ground-based robots cooperate to localize, follow and scan from all sides a moving target. Each robot has a single camera as its only sensor, and they perform collaborative visual SLAM (CoSLAM). We present a simple robot controller that maintains the visual constraints of CoSLAM while orbiting a moving target so as to observe it from all sides. Real-world experiments demonstrate that multiple ground robots can successfully track and scan a moving target.

Video Demo:

Publications
J. Perron*, R. Huang*, J. Thomas, L. Zhang, P. Tan, R. Vaughan,
Orbiting a Moving Target with Multi-Robot Collaborative Visual SLAM,
Workshop on Multi-View Geometry in Robotics (MVIGRO) at the 2015 Robotics: Science and System Conference (RSS'15 workshop), Rome, Italy, July 2015. PDF Project Page Code
(* are first authors of equal contribution)
R. Huang, P. Tan and B. M. Chen,
Monocular vision-based autonomous navigation system on a toy quadcopter in unknown environments,
presented at Proceedings of the 2015 International Conference on Unmanned Aircraft Systems, Denver, USA, pp. 1260-1269, June 2015. PDF Project Page Code
K. Li, R. Huang, S. K. Phang, S. Lai, F. Wang, P. Tan, B. M. Chen and T. H. Lee,
Vision-based autonomous control of an ultralight quadrotor MAV,
presented at the 2014 International Micro Air Vehicle Conference and Competition, Delft, the Netherlands, August 2014. PDF