7.5: Exercises
- Page ID
- 14813
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Take-home lessons
- Features are “interesting” information in sensor data that are robust to variations in rotation and scale as well as noise.
- Which features are most useful depends on the characteristics of the sensor generating the data, the structure of the environment, and the actual application.
- There are many feature detectors available some of which operating as simple filters, others relying on machine learning techniques.
- Lines are among the most important features in mobile robotics as they are easy to extract from many different sensors and provide strong clues for localization.
Exercises
- Think about what information would make good features in different operating scenarios: a supermarket, a warehouse, a cave.
- What other features could you detect using a Hough transform? Can you find parameterizations for a circle, a square or a triangle?
- Do an online search for SIFT. What other similar feature detectors can you find? Which provide source code that you can use online?
- A line can be represented by the function y = mx + c. Then, the Hough-space is given by a 2D coordinate system spanned by m and c.
- Think about a line representation in polar coordinates. What components does the Hough-space consist of in this case?
- Derive a parameterization for a circle and describe the resulting Hough space.