Mostly just linked to CV notes…
- 9.1 Explains that Feature extraction is about information reduction
- 9.2 lists some common features and ways to extract them
- 9.3 covers Line detection specifically
- 9.4 talks about SIFT - just re-routing that to SIFT notes from CV
- 9.5 explains that most of the things done in this could easily be done by NN but still useful to know these things for pre-processing and such
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.