ECE497 SLAM via ROS

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

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• 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

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Highlights

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Theory of Operation

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Work Breakdown

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Future Work

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Conclusions

In order to improve performance once could bolster the sensory profile of the platform. Useful sensors include:

1. Laser scanning range-finder
2. IMU (Inertial measurement unit)
3. Digital Compass
4. GPS

The incorporation of the data gathered from these sensors will improve the robot's model of the world and decrease the uncertainty it has about its location in the model.