ECE497 SLAM via ROS

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Revision as of 17:35, 2 November 2012 by Whiteer (Talk | contribs) (Conclusions)

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Team members: Elias White

Executive Summary

In autonomous navigation understanding the robot's surrounding environment, as well as its position in this environment, is of paramount importance. This project attempts to leverage the open-source efforts resulting in simultaneous localization and mapping (SLAM) algorithms and use them, in collaboration with the Beagleboard -xm, to develop a 3-D model of the world surrounding the board as it moves through space. Obviously the more (quality) sensory data used in a SLAM algorithm the better the results, but at this time a camera will be the only sensor device, although there is the possibility of incorporating a gyroscope. A primary objective of this project is to test the feasibility of using the Beagleboard -xm as the "brain" for an autonomous quad-copter.



Installation Instructions

Give step by step instructions on how to install your project on the SPEd2 image.

  • Include your github path as a link like this: https://github.com/MarkAYoder/gitLearn.
  • Include any additional packages installed via opkg.
  • Include kernel mods.
  • If there is extra hardware needed, include links to where it can be obtained.

User Instructions

Once everything is installed, how do you use the program? Give details here, so if you have a long user manual, link to it here.

Highlights

While there are currently no highlights, this video provides an idea of what I would like to do, although the quality of their results is much higher than I am expecting to achieve.

Theory of Operation

Give a high level overview of the structure of your software. Are you using GStreamer? Show a diagram of the pipeline. Are you running multiple tasks? Show what they do and how they interact.

Work Breakdown

As a solo group I'll be the only one working on this project.

Future Work

Suggest addition things that could be done with this project.

Conclusions