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Proceedings Paper

Overview of machine learning (ML) based perception algorithms for unstructured and degraded visual environments
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Paper Abstract

Machine learning based perception algorithms are increasingly being used for the development of autonomous navigation systems of self-driving vehicles. These vehicles are mainly designed to operate on structured roads or lanes and the ML algorithms are primarily used for functionalities such as object tracking, lane detection and semantic understanding. On the other hand, Autonomous/ Unmanned Ground Vehicles (UGV) being developed for military applications need to operate in unstructured, combat environment including diverse off-road terrain, inclement weather conditions, water hazards, GPS denied environment, smoke etc. Therefore, the perception algorithm requirements are different and have to be robust enough to account for several diverse terrain conditions and degradations in visual environment. In this paper, we present military-relevant requirements and challenges for scene perception that are not met by current state-of-the-art algorithms, and discuss potential strategies to address these capability gaps. We also present a survey of ML algorithms and datasets that could be employed to support maneuver of autonomous systems in complex terrains, focusing on techniques for (1) distributed scene perception using heterogeneous platforms, (2) computation in resource constrained environment (3) object detection in degraded visual imagery.

Paper Details

Date Published: 10 May 2019
PDF: 7 pages
Proc. SPIE 11006, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications, 110061T (10 May 2019); doi: 10.1117/12.2519029
Show Author Affiliations
Priya Narayanan, U.S. Army Research Lab. (United States)
Zhenyu Wu, Texas A&M Univ. (United States)
Heesung Kwon, U.S. Army Research Lab. (United States)
Zhangyang Wang, Texas A&M Univ. (United States)
Raghuveer Rao, U.S. Army Research Lab. (United States)

Published in SPIE Proceedings Vol. 11006:
Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications
Tien Pham, Editor(s)

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