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Low-Cost Localization for Educational Robotic Platforms via an External Fixed-Position Camera

Low-Cost Localization for Educational Robotic Platforms via an External Fixed-Position Camera. NSF Grant OCI-0636235 NSF Grant SCI-0537370. Drew Housten (dth29@drexel.edu) Dr. William Regli (regli@drexel.edu). Pre-College Educational Robotics.

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Low-Cost Localization for Educational Robotic Platforms via an External Fixed-Position Camera

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  1. Low-Cost Localization for Educational Robotic Platforms via an External Fixed-Position Camera NSF Grant OCI-0636235 NSF Grant SCI-0537370 Drew Housten (dth29@drexel.edu) Dr. William Regli (regli@drexel.edu)

  2. Pre-College Educational Robotics • Robotics is an excellent tool to teach AI, Engineering, Math, and Science • Currently, educational system sophistication heavily depends on hardware cost • LEGO NXT (Fairly Cheap, Limited) • AIBO (Expensive, More Sophisticated) • But, cost of the solution matters in pre-college education! • Research does not follow the same trends • Example: DARPA Urban Challenge was mostly a software problem

  3. Pre-College Educational Robotics • Hardware complexity of educational robotics is currently sufficient • However, Software and System complexity of educational robotics is limited • This problem can be addressed by building software tools to bridge the gap • Software tools can be free to educators

  4. Why Localization? • Chose Localization as a starting point • Currently many AI educational projects are limited because the robot does not know where it is • Maze Following • Navigation • Searching • Etc.

  5. Problem of Localization • Current solutions in research: • Odometry • Global Positioning Systems (GPS) • LIDAR • Sonar or Infrared Arrays • Contact Sensors • Fuducials or Landmarks • Cameras • Etc. • Current solutions do not work well for education • Expensive • Complicated to use • Does not work well in typical educational environments

  6. CamLoc (Camera Localization) • Goals of CamLoc • Inexpensive solution to localization • Simple to use • Requires no hardware modifications • Simplistic solution to support teaching the principles to students • Decimeter-level accuracy in localization in an indoor environment

  7. Necessary Hardware Webcam ($50-$150) Total Cost w/o Computer: ~$400 iRobot Roomba ($200) SparkFun Electronics RooTooth ($100) Computer ($500 - $2500)

  8. Technical Approach: Fusion of Odometry & Visual Tracking • Topological Mapping: • Record Robot’s start position in the image frame • Make an action (point turn, drive) • Record odometry distance and heading traveled • Record Robot’s end position in image frame • Add an edge to the Topological Map • Vertices are the image frame positions • Localization: • Search through the Topological map to find a path between the initial position and the current position • Calculate the current position by simulating the actions to travel that path

  9. Results from 3 Runs Square Circuit 39 Actions 12.765 Meters Cloverleaf Circuit 50 Actions 10.885 Meters Pseudo-Random 84 Actions 27.489 Meters Mean Positional Error

  10. Future Work • Enhancements and Improvements to Approach • Build a more complete toolkit to assist robotic educators • Use the solution in a classroom setting • Make the toolkit available for download athttp://gicl.cs.drexel.edu/wiki/LearningRoomba

  11. Questions? ?

  12. Backup

  13. Odometry vs. Topological Map

  14. Vision Tracking Interface

  15. Trial 1: Square Circuit

  16. Trial 2: Cloverleaf Circuit

  17. Trial 3: Pseudo-Random Path

  18. Approach • Goals • Localization to decimeter-level accuracy • Low-cost Solution • Easy to configure / setup / use • Elements of Solution • Odometry • Topological Map • Image Tracking

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