用可穿戴计算机进行野外地质学：Cyborg天体生物学家系统的第一个结果
Field geology with a wearable computer: 1st results of the Cyborg Astrobiologist System
摘要：
我们目前的结果，从第一地质现场测试的“机器人天体生物学家”，这是一个可穿戴的计算机和视频摄像机系统，我们正在使用的测试和训练的计算机视觉系统对有一些自主决策能力的外地地质学家。迄今为止，Cyborg Astrobiologist平台已被用于测试和开发这些算法和系统：机器人获取图像的准马赛克，实时图像分割，以及实时确定图像马赛克中的有趣点。这项工作更多的是对整个系统的测试，而不是对系统的任何一个部分的测试。然而，除了系统本身的概念之外，不寻常的地图（尽管它很简单）是系统的主要创新部分。不常见的地图有助于以与上下文无关的方式确定兴趣点。总体而言，硬件和软件系统运行可靠，计算机视觉算法足以进行第一次现场测试。除了这些实地测试的概念验证方面，这些实地测试的主要结果是列举了我们在未来可以改进的问题，包括：处理结构阴影和微观纹理，以及以智能方式控制相机的变焦镜头。然而，尽管存在这些和其他技术上的不足，这个由配备摄像头的可穿戴计算机及其计算机视觉算法组成的Cyborg天体生物学家系统已经证明了它能够在地质景观中实时找到真正有趣的点，然后以自动化的方式收集有关这些兴趣点的更多信息。我们利用这些功能对地质兴趣点进行自主引导。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
