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Pose Estimation for Robotic Soccer Players in the Context of RoboCup - - PowerPoint PPT Presentation

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MIN Faculty Department of Informatics Pose Estimation for Robotic Soccer Players in the Context of RoboCup Judith Hartfill University of Hamburg Faculty of Mathematics, Informatics and Natural Sciences Department of Informatics Technical

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MIN Faculty Department of Informatics

Pose Estimation for Robotic Soccer Players

in the Context of RoboCup Judith Hartfill

University of Hamburg Faculty of Mathematics, Informatics and Natural Sciences Department of Informatics Technical Aspects of Multimodal Systems

November 28, 2017

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Outline

Motivation RoboCup Pose Estimation Approaches in Robocup Soccer Summary Conclusion and Perspectives References

  • 1. Motivation
  • 2. RoboCup

RoboCup Soccer Leagues

  • 3. Pose Estimation

Pose Estimation Approaches Particle Filter Problems

  • 4. Approaches in Robocup Soccer

Humanoid Kid Size League Standart Platform League

  • 5. Summary
  • 6. Conclusion and Perspectives
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Motivation

Motivation RoboCup Pose Estimation Approaches in Robocup Soccer Summary Conclusion and Perspectives References

Knowing own pose is essential for decision making. How can a robot know its pose on the field?

1 1http://clipart-library.com/images/8iGb5XKbT.jpg

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RoboCup Competitions

Motivation RoboCup Pose Estimation Approaches in Robocup Soccer Summary Conclusion and Perspectives References

◮ International competitions ◮ Since 1996 ◮ 500 teams ◮ Several leagues

2 2http://www.robocup2014.org/wp-content/uploads/2014/04/RCfed_high_M_Transp.png

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RoboCup Industrial Leagues

Motivation RoboCup Pose Estimation Approaches in Robocup Soccer Summary Conclusion and Perspectives References

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3http://robohub.org/robocup-video-series-industrial-league/

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RoboCup Rescue Leagues

Motivation RoboCup Pose Estimation Approaches in Robocup Soccer Summary Conclusion and Perspectives References

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4http://www.robocup2009.org/21-1-robocup%20rescue.html

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RoboCup@Home Leagues

Motivation RoboCup Pose Estimation Approaches in Robocup Soccer Summary Conclusion and Perspectives References

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5https://ispr.info/2011/08/01/robocuphome-2011-when-the-home-help-is-a-robot/

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RoboCup Soccer Leagues

Motivation RoboCup Pose Estimation Approaches in Robocup Soccer Summary Conclusion and Perspectives References

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6https://www.robocupgermanopen.de

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RoboCup Soccer Leagues

Motivation RoboCup Pose Estimation Approaches in Robocup Soccer Summary Conclusion and Perspectives References

◮ Humanoid Leagues

◮ Several sizes ◮ Only humanoid sensors ◮ Humanoid dimensions ◮ Adapted FIFA rules

◮ Standart Platform League

◮ NAO ◮ not humanoid

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Win against FIFA World Cup champion in 2050

7https://robocup.informatik.uni-hamburg.de/wp-content/uploads/2017/07/P1100771-1.jpg

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Pose Estimation Approaches

Motivation RoboCup Pose Estimation Approaches in Robocup Soccer Summary Conclusion and Perspectives References

Pattern Matching [6]

◮ Least-squares

linear regression problem Visual Compass [3]

◮ Visual map ◮ Histogram

Particle Filter [4]

◮ Probabilistic

method

◮ Multiple sensor

inputs

8 9 10

8https://upload.wikimedia.org/wikipedia/commons/b/b0/Linear_least_squares_example2.svg 9http://alife-robotics.co.jp/homepage2018/members2017/icarob/data/html/data/OS_pdf/OS12/OS12-4.pdf 10http://networks.ece.mcgill.ca/sites/default/files/1.png

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Particle Filter

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Particle filter

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Problems in Humanoid Kid Size League

Motivation RoboCup Pose Estimation Approaches in Robocup Soccer Summary Conclusion and Perspectives References

◮ Odometry hard ◮ Bad vision ◮ Computationally limited ◮ Symmetry of the field ◮ Other robots occluding view ◮ ...

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Approaches in Humanoid Kid Size League

Motivation RoboCup Pose Estimation Approaches in Robocup Soccer Summary Conclusion and Perspectives References

◮ Reminder: Odometry hard ◮ Rhoban [2]:

◮ 3D Particle filter ◮ Magnetometer ◮ Field boarders and goals posts ◮ Foot pressure sensors ◮ Action model less erroneous ◮ Visual observations scored

11

11https://www.robocuphumanoid.org/qualification/2017/22ee18648e39f3f656609d932ab6ccaa70a66929/

Rhoban_Fooball_Club_Humanoid_KidSize_regularanddrop_in_2017_TDP.pdf

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Approaches in Humanoid Kid Size League

Motivation RoboCup Pose Estimation Approaches in Robocup Soccer Summary Conclusion and Perspectives References

◮ Reminder: Bad vision ◮ ZJU[5]

◮ Particle filter with sensor

resetting

◮ Input noisy ◮ Propability of particles low ◮ Replace some particles with

noisy ones

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Approaches in Standart Platform League

Motivation RoboCup Pose Estimation Approaches in Robocup Soccer Summary Conclusion and Perspectives References

◮ Improvement: Communication ◮ Camellia Dragons [1]

◮ Observer view robot observing field ◮ Sharing information via WiFi ◮ Resampling particles with additional information ◮ Not natural like in usual soccer Camera image True perspective image

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Pose estimation

12http://alife-robotics.co.jp/homepage2018/members2017/icarob/data/html/data/OS_pdf/OS12/OS12-4.pdf

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Summary

Motivation RoboCup Pose Estimation Approaches in Robocup Soccer Summary Conclusion and Perspectives References

◮ Hardware

◮ Foot pressure sensors: Better data for particle filter

◮ Software

◮ Sensor resetting: Escape from bad estimates ◮ Observer view: Use all capacities

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Conclusion and Perspectives

Motivation RoboCup Pose Estimation Approaches in Robocup Soccer Summary Conclusion and Perspectives References

◮ Particle filter popular and reliable ◮ Workarounds for bad sensor data ◮ Hardware improvements useful ◮ Communication becoming more important ◮ Better computers/ sensors

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Conclusion and Perspectives

Motivation RoboCup Pose Estimation Approaches in Robocup Soccer Summary Conclusion and Perspectives References

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References

Motivation RoboCup Pose Estimation Approaches in Robocup Soccer Summary Conclusion and Perspectives References

[1] Yo Aizawa, Takuo Suzuki, and Kunikazu Kobayashi. Improvement of robot’s self-localization by using observer view positional information, 2017. The 2017 International Conference on Artificial Life and Robotics (ICAROB 2017),

  • Jan. 19-22, Seagaia Convention Center, Miyazaki, Japan.

[2] Julien Allali, Louis Deguillaume, Remi Fabre, Loic Gondry, Ludovic Hofer, Olivier Ly, Steve NGuyen, Gregoire Passault, Antoine Pirrone, and Quentin Rouxel. Rhoban football club: Robocup humanoid kid-size 2016 champion team paper. Springer Berlin Heidelberg, Berlin, Heidelberg, 2016. [3] Peter Anderson and Bernhard Hengst. Fast Monocular Visual Compass for a Computationally Limited Robot, pages 244–255. Springer Berlin Heidelberg, Berlin, Heidelberg, 2014.

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References (cont.)

Motivation RoboCup Pose Estimation Approaches in Robocup Soccer Summary Conclusion and Perspectives References

[4] S. Lenser and M. Veloso. Sensor resetting localization for poorly modelled mobile robots. In Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No.00CH37065), volume 2, pages 1225–1232 vol.2, 2000. [5] Mei WenXing, Pan Yusu, Peng Bo, Jiang ChaoFeng, Liu Yun, and Xiong Rong. Zjudancer team description paper, 2017. RoboCup 2017 Team Description Paper Humanoid Kid-Size League. [6] Thomas Whelan, Sonja Stüdli, John McDonald, and Richard H. Middleton. Efficient Localization for Robot Soccer Using Pattern Matching, pages 16–30. Springer Berlin Heidelberg, Berlin, Heidelberg, 2012.

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