The Cyborg Astrobiologist: testing a novelty detection algorithm on two mobile exploration systems at Rivas Vaciamadrid in Spain and at the Mars Desert Research Station in Utah
Abstract
In previous work, a platform was developed for testing computer-vision algorithms for robotic planetary exploration. This platform consisted of a digital video camera connected to a wearable computer for real-time processing of images at geological and astrobiological field sites. The real-time processing included image segmentation and the generation of interest points based upon uncommonness in the segmentation maps. Also in previous work, this platform for testing computer-vision algorithms has been ported to a more ergonomic alternative platform, consisting of a phone camera connected via the Global System for Mobile Communications (GSM) network to a remote-server computer. The wearable-computer platform has been tested at geological and astrobiological field sites in Spain (Rivas Vaciamadrid and Riba de Santiuste), and the phone camera has been tested at a geological field site in Malta. In this work, we (i) apply a Hopfield neural-network algorithm for novelty detection based upon colour, (ii) integrate a field-capable digital microscope on the wearable computer platform, (iii) test this novelty detection with the digital microscope at Rivas Vaciamadrid, (iv) develop a Bluetooth communication mode for the phone-camera platform, in order to allow access to a mobile processing computer at the field sites, and (v) test the novelty detection on the Bluetooth-enabled phone camera connected to a netbook computer at the Mars Desert Research Station in Utah. This systems engineering and field testing have together allowed us to develop a real-time computer-vision system that is capable, for example, of identifying lichens as novel within a series of images acquired in semi-arid desert environments. We acquired sequences of images of geologic outcrops in Utah and Spain consisting of various rock types and colours to test this algorithm. The algorithm robustly recognized previously observed units by their colour, while requiring only a single image or a few images to learn colours as familiar, demonstrating its fast learning capability.
- Publication:
-
International Journal of Astrobiology
- Pub Date:
- January 2010
- DOI:
- 10.1017/S1473550409990358
- arXiv:
- arXiv:0910.5454
- Bibcode:
- 2010IJAsB...9...11M
- Keywords:
-
- novelty detection;
- Hopfield neural network;
- sceintific autonomy;
- Bluetooth communications;
- phone camera;
- lichens;
- digital microscope;
- wearable computer;
- graphical programming language;
- image segmentation;
- uncommon mapping;
- Mars Desert Research Station;
- Morrison Formation;
- gypsum;
- single-instance learning;
- Computer Science - Computer Vision and Pattern Recognition;
- Astrophysics - Earth and Planetary Astrophysics;
- Astrophysics - Instrumentation and Methods for Astrophysics;
- Computer Science - Machine Learning;
- Statistics - Machine Learning
- E-Print:
- 28 pages, 12 figures, accepted for publication in the International Journal of Astrobiology