Spatio-spectral exploration combining in situ and remote measurements - Robotics Institute Carnegie Mellon University

Spatio-spectral exploration combining in situ and remote measurements

Conference Paper, Proceedings of 29th AAAI Conference on Artificial Intelligence (AAAI '15), pp. 3679 - 3685, 2015

Abstract

Adaptive exploration uses active learning principles to improve the efficiency of autonomous robotic surveys. This work considers an important and understudied aspect of autonomous exploration: in situ validation of remote sensing measurements. We focus on high- dimensional sensor data with a specific case study of spectroscopic mapping. A field robot refines an orbital image by measuring the surface at many wavelengths. We introduce a new objective function based on spectral unmixing that seeks pure spectral signatures to accurately model diluted remote signals. This objective reflects physical properties of the multi-wavelength data. The rover visits locations that jointly improve its model of the environment while satisfying time and energy constraints. We simulate exploration using alternative planning approaches, and show proof of concept results with the canonical spectroscopic map of a mining district in Cuprite, Nevada.

BibTeX

@conference{Thompson-2015-17189,
author = {David R. Thompson and David Wettergreen and Greydon Foil and Padraig Michael Furlong and Anatha Ravi Kiran},
title = {Spatio-spectral exploration combining in situ and remote measurements},
booktitle = {Proceedings of 29th AAAI Conference on Artificial Intelligence (AAAI '15)},
year = {2015},
month = {January},
pages = {3679 - 3685},
}