In the Dead of Winter: Challenging Vision-Based Path Following in Extreme Conditions

Abstract

In order for vision-based navigation algorithms to extend to long-term autonomy applications, they must have the ability to reliably associate images across time. This ability is challenged in unstructured and outdoor environments, where appearance is highly variable. This is especially true in temperate winter climates, where snowfall and low sun elevation rapidly change the appearance of the scene. While there have been proposed techniques to perform localization across extreme appearance changes, they are not suitable for many navigation algorithms such as autonomous path following, which requires constant, accurate, metric localization during the robot traverse. Furthermore, recent methods that mitigate the effects of lighting change for vision algorithms do not perform well in the contrast-limited environments associated with winter. In this paper, we highlight the successes and failures of two state-of-the-art path-following algorithms in this challenging environment. From harsh lighting conditions to deep snow, we show through a series of field trials that there remain serious issues with navigation in these environments, which must be addressed in order for long-term, vision-based navigation to succeed.