A visual compass for robot soccer
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This document summarizes a final year project on developing a visual compass for global localization of a robot soccer player. The student developed a prototype desktop application using OpenCV and SURF/SIFT algorithms to build a panoramic visual map from images and determine the robot's direction by matching query images to the map. Experimental results found errors of 18% for SURF and 29% for SIFT. Future work involves porting the application to the Nao robot and reducing errors by creating visual maps from multiple positions on the pitch.
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A visual compass for robot soccer
- 1. Final Year Project Final Presentation Student: Adil Mahmood Supervisor: Dr John McDonald
- 2. What is RoboCup RoboCup is an annual international robotics soccer competition founded in 1997. The official goal of the RoboCup: robots that are capable of beating the FIFA World Cup winning team by 2050. RoboEireann, Maynooths RoboCup team, have competed in the Standard Platform League (SPL) since 2008.
- 3. Overview of Background Mobile robot localisation can be separated into two separate problems, Local localisation (a.k.a. incremental localisation) the robot has an initial estimate of its pose Global localisation (a.k.a. kidnapped robot) robot doesnt have initial position information
- 4. Estimated line Real Pose Estimated Pose Real line Loop closure correction Local Localisation
- 5. Global Localisation Where Im? Which direction Im facing?
- 6. Research Questions Is visual compass effective to find the direction of robot is currently looking? What is the measurement of success?
- 7. Goals of Project Develop a visual compass for global localisation Images of visual appearance of fixed objects surrounding the pitch (or potential above the pitch) construct a panorama using a sequence of images match query image to the panorama detect features and matched features. Compute the direction of robot
- 8. Approach to Goals Build a visual map using OpenCV and SURF algorithms Visual map represent 360属 find the direction the robot is currently looking.
- 10. Visual Compass
- 13. Implementations Qt Creator, OpenCV, C++ Algirithm images Stitching pipeline Panorama Query image Load into QtDetectorDescriptor Draw keypoint Distance between keypoint Good matches Create, save histogramDirection
- 14. Experimental Results SURF 賊18 % and SIFT 賊29 % of error Reason for large number of error?
- 15. Experimental Results Weakness in technique image29 image30 image35
- 16. Conclusions & Future Work Developed prototype desktop visual compass application Evaluated on real image dataset Problems Encountered Ubuntu OS troubleshooting, QT toolkit, and OpenCV libraries Design the GUI application in QT Creating a histogram Future Work: Port to Nao robot.
- 17. Possible Solution to reduce the error Create a visual map from different position of pitch
- 18. A Visual Compass for Robot Soccer
Editor's Notes
- #3: By the middle of the 21st century, a team of fully油autonomous humanoid robot soccer油players shall win a油soccer油game, complying with the official rules of FIFA, against the winner of the most recent油World Cup.
- #4: localisation problem tracks the pose of the robot over time, where the robot has an initial estimate of its pose, which it updates through the robots odometry and information it gathers for its sensors. The more challenging problem of global localisation occurs where a robot doesnt have initial position information, i.e., they can handle the kidnapped robot problem, in which a robot is kidnapped and carried to some unknown location.
- #7: YES: that it is possible to use a panoramic image representation as an visual map of the area surrounding of a pitch, by matching un-seen images to find the direction the robot is currently looking . A measurement of success is you can take random image from inside the lab for example, and know what direction are you looking for, you know Im pointing in that direction of lab.
- #8: using a sequence of images create a panorama, using panorama and one query image, detect features in both images and find the best matched features.
- #10: This is the final view of project, displaying panorama and query images with feature detected and also shown the number of detected features, and SURF algorithm match the best features, then create the histogram of these good matches. And find the direction of robot from peak of histogram.
- #11: This is the final view of project, displaying panorama and query images with feature detected and also shown the number of detected features, and SURF algorithm match the best features, then create the histogram of these good matches. And find the direction of robot from peak of histogram.
- #12: This is the final view of project, displaying panorama and query images with feature detected and also shown the number of detected features, and SURF algorithm match the best features, then create the histogram of these good matches. And find the direction of robot from peak of histogram.
- #13: This is the final view of project, displaying panorama and query images with feature detected and also shown the number of detected features, and SURF algorithm match the best features, then create the histogram of these good matches. And find the direction of robot from peak of histogram.
- #14: This is the final view of project, displaying panorama and query images with feature detected and also shown the number of detected features, and SURF algorithm match the best features, then create the histogram of these good matches. And find the direction of robot from peak of histogram.
- #15: This is the graph of experimental result, shown the result direction of angle with different algorithm and expected result. By comparing the expected result and actual output result, we find the 18 % error with SURF and 29% error with SIFT algorithm.
- #16: In the previous slide graph shown direction for image29 and image30 both have same direction of view 327 with SIFT. image35 have zero feature detected, direction of view never find with SUF but SIFT detector result 266 degree
- #18: The reason is well have a different visual map those possible covered much more features to compare query image, and reduced the number of error.