Share Email Print
cover

Proceedings Paper • new

Synthetic models for infrared reflectance signatures of micro-particle traces on surfaces
Author(s): Robert Furstenberg; Andrew Shabaev; Christopher A. Kendziora; Christopher Breshike; Tyler J. Huffman; Andrew Kusterbeck; Dawn Dominguez; Samuel G. Lambrakos; R. Andrew McGill
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

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: 17 May 2019
PDF: 9 pages
Proc. SPIE 11010, Chemical, Biological, Radiological, Nuclear, and Explosives (CBRNE) Sensing XX, 110100K (17 May 2019); doi: 10.1117/12.2519041
Show Author Affiliations
Robert Furstenberg, U.S. Naval Research Lab. (United States)
Andrew Shabaev, Leidos, Inc. (United States)
Christopher A. Kendziora, U.S. Naval Research Lab. (United States)
Christopher Breshike, American Society for Engineering Education (United States)
Tyler J. Huffman, National Research Council (United States)
Andrew Kusterbeck, Nova Research, Inc. (United States)
Dawn Dominguez, Nova Research, Inc. (United States)
Samuel G. Lambrakos, U.S. Naval Research Lab. (United States)
R. Andrew McGill, U.S. Naval Research Lab. (United States)


Published in SPIE Proceedings Vol. 11010:
Chemical, Biological, Radiological, Nuclear, and Explosives (CBRNE) Sensing XX
Jason A. Guicheteau; Chris R. Howle, Editor(s)

© SPIE. Terms of Use
Back to Top