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

Drew Housten (dth29@drexel.edu)Dr. William Regli (regli@drexel.edu)

NSF Grant OCI-0636235NSF Grant SCI-0537370

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

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

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.

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

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

Necessary Hardware

iRobot Roomba ($200)

SparkFun Electronics RooTooth ($100)

Computer ($500 - $2500)

Webcam ($50-$150)

Total Cost w/o Computer: ~$400

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

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

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

Questions?

Backup

Odometry vs. Topological Map

Vision Tracking Interface

Trial 1: Square Circuit

Trial 2: Cloverleaf Circuit

Trial 3: Pseudo-Random Path

Approach Goals

Localization to decimeter-level accuracy Low-cost Solution Easy to configure / setup / use

Elements of Solution Odometry Topological Map Image Tracking