Improving Visual Feature Extraction in Glacial Environments

TL;DR

This study demonstrates that using near-infrared imaging with a custom camera rig significantly enhances the quantity and quality of visual features in icy environments, improving visual navigation for glacial robots.

Contribution

We introduce a novel approach of applying near-infrared filters to improve feature extraction in icy terrains, aiding autonomous glacial exploration.

Findings

NIR imaging increases feature quantity and quality.

Improved visual odometry accuracy in icy environments.

NIR-based features outperform visible light features in feature-richness.

Abstract

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.