  We present results from the first geological field tests of the `Cyborg
Astrobiologist', which is a wearable computer and video camcorder system that
we are using to test and train a computer-vision system towards having some of
the autonomous decision-making capabilities of a field-geologist. The Cyborg
Astrobiologist platform has thus far been used for testing and development of
these algorithms and systems: robotic acquisition of quasi-mosaics of images,
real-time image segmentation, and real-time determination of interesting points
in the image mosaics. This work is more of a test of the whole system, rather
than of any one part of the system. However, beyond the concept of the system
itself, the uncommon map (despite its simplicity) is the main innovative part
of the system. The uncommon map helps to determine interest-points in a
context-free manner. Overall, the hardware and software systems function
reliably, and the computer-vision algorithms are adequate for the first field
tests. In addition to the proof-of-concept aspect of these field tests, the
main result of these field tests is the enumeration of those issues that we can
improve in the future, including: dealing with structural shadow and
microtexture, and also, controlling the camera's zoom lens in an intelligent
manner. Nonetheless, despite these and other technical inadequacies, this
Cyborg Astrobiologist system, consisting of a camera-equipped wearable-computer
and its computer-vision algorithms, has demonstrated its ability of finding
genuinely interesting points in real-time in the geological scenery, and then
gathering more information about these interest points in an automated manner.
We use these capabilities for autonomous guidance towards geological
points-of-interest.
