7.2: Features

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Lines are particularly useful features for localization and can correspond to walls in laser scans, markers on the floor or corners detected in a camera image. Whereas a Sobel filter (Section 6.3.1) can help us to highlight lines and edges in images, additional algorithms are needed to extract structured information such as the orientation and position of a line with respect to the robot.

A desirable property of a feature is that its extraction is repeatable and robust to rotation, scale, and noise in the data. We need feature detectors that can extract the same feature from sensor data, even if the robot has slightly turned or moved farther or closer to the feature. There are many feature detectors available that accomplish this, prominent examples are the Harris corner detector (essentially detecting points in the image where vertical and horizontal lines cross) and the SIFT feature detector. Feature detection is important far beyond robotics and is for example used in hand-held cameras that can automatically stitch images together. Here, feature detectors will “fire” on the same features in two images taken from slightly different perspectives, which allows the camera to calculate the transformation between the two.

This chapter focuses on two important classes of features: line features and scale-invariant features in images (SIFT). Both features provide tangible example for the least-squares and RANSAC algorithms, which are also introduced in this chapter. Both features are representative for a large class of detectors, and have been chosen for their simplicity, providing a basis for under standing the function of more complex feature detectors.