Difference between revisions of "Localizing with AprilTags"
(→How the Module Performs Localization) |
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(explanation of diagram here) | (explanation of diagram here) | ||
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| + | == Obtaining Local Position == | ||
| + | TODO | ||
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| + | == Obtaining Global Position == | ||
| + | TODO | ||
| + | |||
| + | == Example of Localization == | ||
| + | |||
| + | (Real World Robot Demo Video with One Glyph) | ||
| + | |||
| + | (Short Explanation) | ||
| + | |||
| + | (Demo Code) | ||
== TODO == | == TODO == | ||
* Explanation of how we localize | * Explanation of how we localize | ||
* Demo video if real world robot demonstrating this behavior | * Demo video if real world robot demonstrating this behavior | ||
Revision as of 12:57, 6 February 2015
Our method of indoor localization utilizes a module with a ceiling-facing camera that recognizes glyph markers on the ceiling. The glyph markers on the ceiling each have unique IDs corresponding to positions in the global map of the area that the module is localizing in. In order to make our localization method possible, we needed to determine a practical glyph recognition system to use. We have chosen to use AprilTags as our glyph recognition system due to its robustness in accurate recognition of its tags. The AprilTags system provides quick scale-invariant and rotation-invariant recognition of its tags and will therefore prove very useful to our indoor localization project as our chosen glyph recognition system. AprilTags was developed at the University of Michigan by Professor Edwin Olson. Check out the AprilTags wiki here.
Contents
How the Module Performs Localization
(diagram here)
(explanation of diagram here)
Obtaining Local Position
TODO
Obtaining Global Position
TODO
Example of Localization
(Real World Robot Demo Video with One Glyph)
(Short Explanation)
(Demo Code)
TODO
- Explanation of how we localize
- Demo video if real world robot demonstrating this behavior
