Outdoor Path Labeling Using Polynomial Mahalanobis Distance

Autonomous robot navigation in outdoor environments remains a challenging and unsolved problem. A key issue is our ability to identify safe or navigable paths far enough ahead of the robot to allow smooth trajectories at acceptable speeds. Colour or texture-based labeling of safe path regions in image sequences is one way to achieve this far field prediction. A challenge for classifiers identifying path and nonpath regions is to make meaningful comparisons of feature vectors at pixels or over a window. Most simple distance metrics cannot use all the information available and therefore the resulting labeling does not tightly capture the visible path. We introduce a new Polynomial Mahalanobis Distance and demonstrate its ability to capture the properties of an initial positive path sample and produce accurate path segmentation with few outliers. Experiments show the method’s effectiveness for path segmentation in natural scenes using both colour and texture feature vectors. The new metric is compared with classifications based on Euclidean and standard Mahalanobis distance and produces superior results.