Coping with imbalanced training data for improved terrain prediction in autonomous outdoor robot navigation

Autonomous robot navigation in unstructured outdoor environments is a challenging and largely unsolved area of active research. The navigation task requires identifying safe, traversable paths that allow the robot to progress towards a goal while avoiding obstacles. Machine learning techniques are well adapted to this task, accomplishing near-to-far learning by training appearance-based models using near-field stereo readings in order to predict safe terrain and obstacles in the far field. However, these methods are subject to degraded performance when training data sets exhibit class imbalance, or skew, where data instances of one class outnumber those in another. In such scenarios, classifiers can be overwhelmed by the majority class, and will tend to ignore the minority class. In this paper, we show that typical outdoor terrain scenarios are associated with training data imbalance, and examine the impact of using undersampling, oversampling, SMOTE, and biased penalties techniques to correct for imbalance in stereo-derived training data. We conduct a statistically significant, repeated measures empirical evaluation and demonstrate improved far-field terrain prediction performance when using such methods for handling class imbalance versus taking no corrective action at all.