Lab Meeting 3/29, 2010 (swem): MonoSLAM: Real-Time Single Camera SLAM

MonoSLAM: Real-Time Single Camera SLAM

Andrew J. Davison, Ian D. Reid, Member, IEEE, Nicholas D. Molton, and Olivier Stasse, Member, IEEE

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, VOL. 29, NO. 6, JUNE 2007

[link]

Abstract—We present a real-time algorithm which can recover the 3D trajectory of a monocular camera, moving rapidly through a

previously unknown scene. Our system, which we dub MonoSLAM, is the first successful application of the SLAM methodology from

mobile robotics to the “pure vision” domain of a single uncontrolled camera, achieving real time but drift-free performance inaccessible

to Structure from Motion approaches. The core of the approach is the online creation of a sparse but persistent map of natural

landmarks within a probabilistic framework. Our key novel contributions include an active approach to mapping and measurement, the

use of a general motion model for smooth camera movement, and solutions for monocular feature initialization and feature orientation

estimation. Together, these add up to an extremely efficient and robust algorithm which runs at 30 Hz with standard PC and camera

hardware. This work extends the range of robotic systems in which SLAM can be usefully applied, but also opens up new areas. We

present applications of MonoSLAM to real-time 3D localization and mapping for a high-performance full-size humanoid robot and live

augmented reality with a hand-held camera.

NTU talk: Image-Based Mobile Robot Navigation Design

Title: Image-Based Mobile Robot Navigation Design
Speaker: Prof. Kai-Tai Song, National Chiao Tung University
Time: 2:20pm, April 2 (Fri), 2010
Place: Room 103, CSIE Building


Abstract: Autonomous navigation is a basic function of every mobile robot. It is important for a mobile robot to travel and arrive at a desired location autonomously. In order to accomplish this function, the robot needs to observe its surroundings and locate itself through acquired environmental information. Various sensors have been utilized for acquiring information from environment. Among these perception sensors, image-based systems are most promising for practical application in daily-life scenarios. In this talk, I will first give a general perspective of domestic and service robots today. Then the design issues of mobile robot navigation will be discussed. Two approaches to visual navigation design will be presented. One employs an omni-directional camera, the other uses a normal webcam. In this presentation, the design and implementation of the navigation system will be discussed. Some interesting video clips of autonomous mobile robots will be presented.

Short Biography: Kai-Tai Song received his Ph.D. degree in mechanical engineering from Katholieke Universiteit Leuven, Belgium in 1989. Since 1989 he has been a faculty member and is currently a Professor with National Chiao Tung University (NCTU). From 2007 to 2009, he served as the Associate Dean of the R & D Office of NCTU. He is currently the Director of Institute of Electrical and Control Engineering of NCTU. He has served as the Chairman of IEEE Robotics & Automation Chapter, Taipei Section in the term of 1999. His is a directorate of Robotics Association Taiwan, Taiwan Robotics Society, Taiwan Association of System Science and Engineering, and Chinese Automatic Control Society.
His areas of research interest include mobile robotics, image processing, visual tracking, human-robot interaction, and mechatronics.

Lab Meeting 3/23, 2010 (fish60): Learning to Search: Functional Gradient Techniques for Imitation Learning

I will try to present this one:

Learning to Search: Functional Gradient Techniques for Imitation Learning
Nathan Ratliff, David Silver, J. Andrew Bagnell

Submitted to Autonomous Robotics Special Issue on Robot Learning, 2009

[download draft]

Abstract:
While planning algorithms have shown success in many real-world applications ranging from legged locomotion to outdoor unstructured navigation, such algorithms rely on fully specified cost functions that map sensor readings and environment models to quantifiable costs. Such cost functions are usually manually designed and programmed. Recently, a set of techniques has been developed that explore learning these functions from expert human demonstration. These algorithms apply an inverse optimal control approach to find a cost function for which planned behavior mimics an expert's demonstration.
The work we present extends the Maximum Margin Planning (MMP) frame- work to admit learning of more powerful, non-linear cost functions. These algorithms, known collectively as LEARCH (LEArning to seaRCH ), are simpler to implement than most existing methods, more efficient than previous attempts at non-linearization, more naturally satisfy common constraints on the cost function, and better represent our prior beliefs about the function's form.

NTU talk: 3D Reconstruction from Images

Title: 3D Reconstruction from Images

Shang-Hong Lai
Department of Computer Science
National Tsing Hua University

Time: 2:20pm, March 26 (Fri), 2010
Place: Room 103, CSIE Building

Abstract: The goal of 3D computer vision is to recover real-world three-dimensional information of the scene or objects from 2D images. There have been many different approaches with various image acquisition set-ups to achieve the 3D reconstruction in computer vision. In this talk, I will discuss some 3D reconstruction systems and some researches on 3D reconstruction in my lab. I will present our researches on 3D reconstruction from multi-view images, two-view (stereo) images, and a single image. Some 3D reconstruction results will be shown to demonstrate the performance of our systems.

Bio: Shang-Hong Lai received the BS and MS degrees in electrical engineering from National Tsing Hua University in 1986 and 1988, respectively. He also received the PhD degree from University of Florida, Gainesville, USA, in 1995. He worked for Siemens Corporate Research in Princeton, USA, as a research scientist from 1995 to 2000. Then, Dr. Lai returned to Taiwan to join department of computer science, National Tsing Hua University, as a faculty member. He is currently a professor and associate chair in the same department.