LowCost Localization for Educational Robotic Platforms via an External FixedPosition Camera

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Low-Cost Localization for Educational Robotic
Platforms via an External Fixed-Position Camera
NSF Grant OCI-0636235 NSF Grant SCI-0537370

• Drew Housten (dth29_at_drexel.edu)
• Dr. William Regli (regli_at_drexel.edu)

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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
Webcam (50-150)
Total Cost w/o Computer 400
iRobot Roomba (200)
SparkFun Electronics RooTooth (100)
Computer (500 - 2500)
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Technical Approach Fusion of Odometry Visual
Tracking

• Topological Mapping
• Record Robots start position in the image frame
• Make an action (point turn, drive)
• Record odometry distance and heading traveled
• Record Robots 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

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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
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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