CS7635 Final Project Presentation
Robot Localization - Michael Kaess
Background
Goal: Robot Localization without active sensors (laser, sonar), only based on vision.
The Robot
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ATRV-Jr mobile robot platform with omnicam mounted on top.
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Omnicam Image
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Omnicam view of a the 3rd floor entrance of the MaRC building. |
Locations of Sample Images
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The dots represent locations at which sample images were taken. |
How to work with this huge amount of data? Some way of compression is necessary...
PCA
Even for standard PCA it's too much data, need tricks.
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Mean and first 9 eigenimages. |
Used 24 most significant eigenvectors.
Projected all sample and test images into this eigenspace: Only 24 values left per image.
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Probability distribution of robot location using PCA,
orientation of the robot is provided. |
But in general the orientation is not known...
Approaches:
- Standard: Create eigenspace with sample images rotated to different angles - too complex
- Tricks: Use corridor orientation - didn't really work very well
- Condensation: Plug in results from above in Monte-Carlo-Algorithm - good results
Condensation
Algorithm
Start with random x,y,a-samples (here 2000).
Do the following steps for each input image (better: its projection):
Prediction Phase
Apply motion model.
Update Phase
Weight each sample by its likelihood given the observation.
Resample from weighted samples.
Results for tracking (local localization) and global localization
Tracking: Quicktime (1.2MB)
Global Localization: Quicktime (1.5MB), animated GIF (340kB)
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Last modified: Wed May 1 21:40:43 EDT 2002