An analysis on data curation using mobile robots for learning tasks in complex environments

Julia Donlon; Matthew Young; Maggie Wigness; Cory Hayes

doi:10.1117/12.2518546

10 May 2019 An analysis on data curation using mobile robots for learning tasks in complex environments

Julia Donlon, Matthew Young, Maggie Wigness, Cory Hayes

Author Affiliations +

Proceedings Volume 11006, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications; 110060H (2019) https://doi.org/10.1117/12.2518546
Event: SPIE Defense + Commercial Sensing, 2019, Baltimore, MD, United States

Abstract

Commercial Artificial Intelligence (AI), e.g., the self driving car industry, is often used in predictable settings, with structured surroundings. Significant AI and Machine Learning (ML) progress, particularly in visual perception, has been made in these settings with the use of large publicly available datasets. However, there still exists a prevalent domain mismatch between this data and military relevant environments. In this work we begin to analyze the importance of mobile robot platform design and heterogeneity to effectively collect data more representative of the military domain. The framework of our research is rooted in the importance of expressing constantly changing, yet repeated conditions, with disadvantageous lighting and perspectives in highly unstructured environments.

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Julia Donlon, Matthew Young, Maggie Wigness, and Cory Hayes "An analysis on data curation using mobile robots for learning tasks in complex environments", Proc. SPIE 11006, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications, 110060H (10 May 2019); https://doi.org/10.1117/12.2518546

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