ECE497 SLAM via ROS
Team members: Elias White
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.
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.
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.
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.
As a solo group I'll be the only one working on this project.
Suggest addition things that could be done with this project.
In order to improve performance once could bolster the sensory profile of the platform. Useful sensors include:
- Laser scanning range-finder
- IMU (Inertial measurement unit)
- Digital Compass
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.