Visually aided feature extraction from 3D range data

Robust feature extraction within 3D environments is a crucial requirement for many autonomous robotic and tracking applications. 3D Laser range finders and cameras provide extremely rich data about an environment. However, the algorithms which attempt to compress the vast data sets produced by these sensors into features, tend to be fragile in the presence of sensor noise, or computationally expensive. This paper presents a 3D feature extraction technique which greatly compresses 3D range data based on principal component analysis (PCA). PCA can provide a greatly compressed vector set, representing the dominant directions of data points, thus grouping them into planes or lines. It is shown however, that the naive application of PCA to full, 3D, point cloud data sets, results in a poor representation of the dominant data directions. Therefore, a combination of a panoramic camera and 3D laser range finder is used to extract robust planes from 3D range data. The panoramic camera image is first filtered with the Mean Shift algorithm to smooth segments within it, whilst preserving the integrity of the segment edges. These segments are then used to guide the PCA, through an approximate image to range space calibration, to act on the corresponding individual segments of range data. The application of PCA to segmented subsets of 3D point cloud data sets, will be shown to be robust for the detection of planes in both indoor and urban, outdoor environments.