Adapting Polynomial Mahalanobis Distance for Self-Supervised Learning in an Outdoor Environment

This paper addresses the problem of autonomous navigation of UGV in an unstructured environment. Generally, state-of-the-art approaches use color based segmentation of road/non-road regions in particular. There arises an important question, how is the distance between an input pixel and a color model measured. Many algorithms employ Mahalanobis distance, since Mahalanobis distance better follows the data distribution, however it is assumed, that the data points have a normal distribution. Recently proposed Polynomial Mahalanobis Distance (PMD) represents more discriminative metric, which provides superior results in an unstructured terrain, especially, if the road is barely visible even for humans. In this paper, we discuss properties of the Polynomial Mahalanobis Distance, and propose a novel framework - A Three Stage Algorithm (TSA), which deals with both, picking of suitable data points from the training area as well as self-supervised learning algorithm for long-term road representation.