Improving Visual Feature Extraction in Glacial Environments

Autor: Shoya Higa, Aaron Parness, Steven Morad, Kobus Barnard, Russell C. Smith, Jeremy Nash
Jazyk: angličtina
Rok vydání: 2019
Předmět:
FOS: Computer and information sciences
0209 industrial biotechnology
Control and Optimization
010504 meteorology & atmospheric sciences
Machine vision
Computer science
Computer Vision and Pattern Recognition (cs.CV)
Feature extraction
Biomedical Engineering
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
Computer Science - Computer Vision and Pattern Recognition
Scale-invariant feature transform
Terrain
02 engineering and technology
01 natural sciences
Computer Science - Robotics
020901 industrial engineering & automation
Artificial Intelligence
Computer vision
Visual odometry
0105 earth and related environmental sciences
Ground truth
Orientation (computer vision)
business.industry
Mechanical Engineering
Computer Science Applications
Human-Computer Interaction
Control and Systems Engineering
Feature (computer vision)
Computer Vision and Pattern Recognition
Artificial intelligence
business
Robotics (cs.RO)
Popis: 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.
Databáze: OpenAIRE
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