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Shoya Higa

4 accepted papers

2020

Improving Visual Feature Extraction in Glacial Environments

RA-L 2020

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

Cited by 3SourceScholar
2020

Virtual IR Sensing for Planetary Rovers: Improved Terrain Classification and Thermal Inertia Estimation

RA-L 2020

Terrain classification is critically important for Mars rovers, which rely on it for planning and autonomous navigation. On-board terrain classification using visual information has limitations, and is sensitive to illumination conditions. Classification can be improved if one fuses visual imagery w

Cited by 10SourceScholar
2019

Vision-Based Estimation of Driving Energy for Planetary Rovers Using Deep Learning and Terramechanics

RA-L 2019

This letter presents a prediction algorithm of driving energy for future Mars rover missions. The majority of future Mars rovers would be solar-powered, which would require energy-optimal driving to maximize the range with limited energy. The essential and arguably the most challenging technology fo

Cited by 46SourceScholar
2016

Measurement of stress distributions of a wheel with grousers traveling on loose soil

ICRA 2016

A wheeled mobile robot traveling on loose soil has wheels with grousers (i.e., lugs) on their surface to improve its mobility performance. Although previous studies have analysis and modeling of the mobility performance of a wheel with grousers, most of them did not cover the stress distribution of

Cited by 9SourceScholar