Outdoor World Modeling by Intelligent Integration of Multi-Visual Information

在这个项目中，我们提出了一种场景解释的方法，它动态地集成多视觉传感器数据，如强度和范围图像到场景描述中，根据传感器数据处理的中间结果，通过使用场景中的对象的知识和个人的感觉数据的属性获得。在第一年中，我们发现，几何建模的一个对象类，通过使用多视觉信息是有用的提取对象在这一类和推理的空间关系的对象在场景中。今年，我们扩展了我们的方法，使其可以适用于各种对象类。也就是说，我们开发了一个模型驱动的空间推理系统，从背景中提取各种对象，并确定这些对象的几何结构和未探索的区域以及。研究结果如下：（1）对于立体彩色图像，基于特征的立体匹配方法得到的距离数据是稀疏的，不足以进行场景分割。然后，我们利用区域分割的结果，通过使用的颜色信息，这是有用的恢复场景结构的平面补丁在视差空间中提取。结果，粗略地恢复了场景结构，但是需要更精确的范围信息来确定场景结构的细节。(2)在一对密集的距离数据和强度图像的情况下，我们确定的参数模型的参数从距离数据估计的平面补丁的不确定性。结果表明，不确定度取决于两种误差源：概然误差和系统误差。我们揭示了它们之间的关系，并将其应用于物体识别问题。

In this project, we proposed a method of scene interpretation which dynamically integrates multi-visual sensory data such as intensity and range images into the scene descriptions according to the intermediate results of the sensory data processings that are obtained by using the knowledge of objects in the scene and the properties of the individual sensory data. In the first year, we found that geometric modeling of one object class by using multi-visual information was useful for extraction of the objects in this class and for reasoning of spatial relationships between objects in the scene. This year, we extended our method so that it could be applicable to various kindsof object classes. That is, we developed a model-driven spatial reasoning system which extracted various kinds of objects from the background and determined the geometric structures of these objects and unexplored regions as well. Research results are as follows:(1) In the case of stereo-color images, the range data obtained from a feature based stereo matching method are sparse, therefore they are not sufficient for segmentation of the scene. Then, we utilized the result of region segmentation by using the color information which was useful to recover the scene structure in terms of planar patches extracted in the disparity space. As a result, the scene structure was roughly recovered, but more accurate range information is needed to determine the details of the scene structure.(2) In the case of a pair of dense range data and an intensity image, we determined the parameters of the parametric model from the uncertainties of planar patches estimated from the range data. It has been shown that the uncertainty depends on two kinds of error sources: probable and systematic errors. We revealed their relationships and applied it to the problem of object recognition.