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David R. Thompson, Trey Smith, and David S. Wettergreen. In Proc. Lunar and Planetary Science Conf. (LPSC), March 2006.
Planetary rovers facing tight bandwidth constraints can return more useful science if they are able to prioritize the most interesting data for downlink. We present a technique for choosing a subset of data from a series of images collected during traverse. Our system is context-sensitive, using a Hidden Markov Model to prioritize images that are anomalous with respect to neigboring images in the series. The system was applied to image series collected in the Atacama Desert of Chile; the context-sensitive approach improved the system's ability to select images containing anomalous objects such as large rocks and plants.
@inproceedings{thompson06:novelty,
author = {David R. Thompson and Trey Smith and David S. Wettergreen},
title = {Autonomous Detection of Novel Biologic and Geologic Features in {Atacama Desert} Rover Imagery},
booktitle = {Proc. Lunar and Planetary Science Conf. (LPSC)},
month = {March},
year = 2006,
}
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