Low-Cost Localization for Educational Robotic Platforms via an External Fixed-Position Camera
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Low-Cost Localization for Educational Robotic Platforms via
an External Fixed-Position Camera
Drew Housten ([email protected])Dr. William Regli ([email protected])
NSF Grant OCI-0636235NSF Grant SCI-0537370
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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
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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
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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.
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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
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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
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Necessary Hardware
iRobot Roomba ($200)
SparkFun Electronics RooTooth ($100)
Computer ($500 - $2500)
Webcam ($50-$150)
Total Cost w/o Computer: ~$400
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Technical Approach: Fusion of Odometry & Visual Tracking Topological Mapping:
1) Record Robot’s start position in the image frame2) Make an action (point turn, drive)3) Record odometry distance and heading traveled4) Record Robot’s end position in image frame5) Add an edge to the Topological Map
Vertices are the image frame positions Localization:
1) Search through the Topological map to find a path between the initial position and the current position
2) Calculate the current position by simulating the actions to travel that path
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Results from 3 Runs
Square Circuit39 Actions
12.765 Meters
Cloverleaf Circuit50 Actions
10.885 Meters
Pseudo-Random84 Actions
27.489 Meters
Mean Positional Error
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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
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Questions?
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Backup
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Odometry vs. Topological Map
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Vision Tracking Interface
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Trial 1: Square Circuit
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Trial 2: Cloverleaf Circuit
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Trial 3: Pseudo-Random Path
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Approach Goals
Localization to decimeter-level accuracy Low-cost Solution Easy to configure / setup / use
Elements of Solution Odometry Topological Map Image Tracking