  Glacial science could benefit tremendously from autonomous robots, but
previous glacial robots have had perception issues in these colorless and
featureless environments, specifically with visual feature extraction. This
translates to failures in visual odometry and visual navigation. Glaciologists
use near-infrared imagery to reveal the underlying heterogeneous spatial
structure of snow and ice, and we theorize that this hidden near-infrared
structure could produce more and higher quality features than available in
visible light. We took a custom camera rig to Igloo Cave at Mt. St. Helens to
test our theory. The camera rig contains two identical machine vision cameras,
one which was outfitted with multiple filters to see only near-infrared light.
We extracted features from short video clips taken inside Igloo Cave at Mt. St.
Helens, using three popular feature extractors (FAST, SIFT, and SURF). We
quantified the number of features and their quality for visual navigation by
comparing the resulting orientation estimates to ground truth. Our main
contribution is the use of NIR longpass filters to improve the quantity and
quality of visual features in icy terrain, irrespective of the feature
extractor used.
