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Constructing environment maps with an active vision system through information assimilation.

机译：通过信息吸收利用主动视觉系统构建环境图。

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This dissertation describes an approach for constructing an environment map by multi-sensory information assimilation. The approach has been implemented using a binocular stereo vision system mounted on a mobile robot. The focus of this work is on the development of a complete system for constructing an environment map, dealing with issues of sensor modeling, data acquisition, data transformation, data registration, data fusion, robustness, and computational tractability. We draw on several fundamental results from estimation theory and robust statistics.; We take the view that the approach used in a given situation should be determined by the application and the nature of the data that can be extracted from the available sensors. We first address the problem of building a coarse map of the environment, in the form of a certainty grid, using dynamic stereo. We present an approach for multi-sensory depth information assimilation based on Dempster-Shafer theory for evidential reasoning. This approach is suited for assimilating depth related information from physically different sensors. This approach provides a mechanism to explicitly model ignorance which is desirable when dealing with an unknown environment and offers some representational advantages over the traditional Bayesian approach. We then deal with the problem of constructing high-resolution environment maps from pixel-level data. The representation sought here is in the form of a dense stereo disparity map which is suitable for highly textured scenes. This representation could be useful for extracting higher level features, in particular surface patches. Finally, we deal with the problem of recovering a wire-frame description of an environment which is suitable for scenes containing mostly polyhedral objects. We present a segment-based stereo matching algorithm designed for dynamic stereo sequences. This algorithm utilizes a belief function based approach to computer a reliable wire-frame description. This representation combined with geometric reasoning and model-based vision techniques could be useful for object recognition tasks.
机译：本文介绍了一种通过多感觉信息同化构造环境图的方法。该方法已使用安装在移动机器人上的双目立体视觉系统实现。这项工作的重点是开发用于构建环境图的完整系统，处理传感器建模，数据采集，数据转换，数据注册，数据融合，鲁棒性和计算可处理性等问题。我们从估计理论和稳健统计中得出一些基本结果。我们认为，在给定情况下使用的方法应由应用程序和可以从可用传感器中提取的数据的性质决定。我们首先解决使用动态立体以确定性网格形式构建环境的粗略地图的问题。我们提出了一种基于Dempster-Shafer理论进行证据推理的多传感器深度信息同化方法。该方法适合于从物理上不同的传感器吸收与深度有关的信息。这种方法提供了一种显式建模无知的机制，这在处理未知环境时是理想的，并且提供了优于传统贝叶斯方法的一些代表性优势。然后，我们处理从像素级数据构造高分辨率环境图的问题。此处寻求的表示形式是密集立体视差图的形式，适用于高度纹理化的场景。该表示对于提取更高级别的特征（尤其是表面补丁）可能很有用。最后，我们要解决的问题是恢复适用于主要包含多面体对象的场景的环境的线框描述。我们提出了一种针对动态立体声序列设计的基于片段的立体声匹配算法。该算法利用基于信念函数的方法来计算可靠的线框描述。这种表示与几何推理和基于模型的视觉技术相结合，可能对对象识别任务很有用。

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