CS393R: Autonomous Robots -- Resources
Naos and UT Austin Villa
Week 0: Class Overview
Week 1: Vision basics
- Slides from Tuesday: intro slides.
the ones on physical cameras.
- Slides from Thursday from CMU.
- Slides from Thursday on autonomous
color learning and illumination invariance.
- The videos shown in class:
- The CMVision page.
- Research pages on UT Austin Villa color mapping, autonomous color learning, and follow-ups:
autonomous color learning;
learning with illumination invariance;
- Work by Dan Stronger on Model-based vision.
The UT Austin Villa 2003 Four-Legged Team, Extended version
The University of Texas at Austin, Department of Computer Sciences, AI Laboratory Tech report UT-AI-TR-03-304.
Read Sections 4, 4.1-4.3, 14.
The UT Austin Villa 2004 RoboCup Four-Legged Team: Coming of Age
Read Sections 3, 3.1,3.2 (and the first couple of
appendices if you're interested)
Layered Color Precision for a Self-Calibrating Vision
Bayesian Color Estimation for Adaptive Vision-based Robot Localization.
D. Schulz and D. Fox
Proceedings of IROS, 2004.
B-Human Team Report and Code Release 2011.
Thomas Röfer et al.
Color Learning on a Mobile Robot:
Towards Full Autonomy under Changing Illumination
Mohan Sridharan and Peter Stone.
In The 20th
International Joint Conference on Artificial Intelligence, pp. 2212
Autonomous Color Learning on a Mobile Robot
Mohan Sridharan and Peter Stone
In Proceedings of the Twentieth National Conference on Artificial Intelligence, July 2005.
UT Austin Villa 2013 - Advances in Vision, Kinematics, and Strategy: Paper, Slides
Jacob Menashe et al.
- some vision-related papers from the 2015 RoboCup Symposium
Week 2: Introduction to motion control
Week 3: Motion control continued
Week 4: Probability/Sensing
Week 5: Kalman Filters
Week 6: Localization
- Slides from chapter 4 of the book: (ppt).
- Slides about the legged localization paper below: (pdf).
- Slides about the Hester and Stone paper below: (pdf).
- Some videos from the textbook authors.
- Practical Vision-Based Monte Carlo Localization on a Legged Robot.
Mohan Sridharan, Gregory Kuhlmann, and Peter Stone.
IEEE International Conference on Robotics and Automation, April 2005.
- Negative Information and Line Observations for Monte Carlo Localization.
Todd Hester and Peter Stone.
In IEEE International Conference on Robotics and Automation, May 2008.
- Adapting the
Particle Size in Particle Filters Through KLD-Sampling
In the International Journal of Robotic Research,
(An excellent description of robot localization - has some overlap with the textbook)
Vision-Based Fast and Reactive Monte-Carlo Localization
Thomas Roefer and Matthias Jungel.
In the IEEE International
Conference on Robotics and Automation, ICRA, 2003.
(Another team's implementation details are in Sections III and IV)
- Fast and
Robust Edge-Based Localization in the Sony Four-Legged Robot League
Thomas Roefer and Matthias Jungel.
In the Seventh
International RoboCup Symposium, 2003.
(On using field edges in
Use Of What You Don't see: Negative Information in Markov Localization
Jan Hoffmann, Michael Spranger, Daniel Gohring and Matthias Jungel.
In the IEEE International Conference on Intelligent Robots and Systems, IROS, 2005.
(Recent article on using negative information in localization)
Simultaneous Localization and Mapping (SLAM): Part I The Essential Algorithms
Hugh Durrant-Whyte and Tim Bailey.
- Multiple Model Kalman Filters: A Localization Technique for RoboCup Soccer
Quinlan and Middleton.
Week 7: Action and sensor models
Week 8: Path Planning
Week 9: Behavior Architectures
Week 10: Walking
Week 11: Multi-Robot Coordination
Week 12: Applications
Applications papers and related resources from a previous version of this course
Week 13: Social Implications
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