摘要翻译：
提出了一种利用全向图像和数字地形图中相应特征进行位姿和运动估计的算法。在以前的论文中，考虑了规则摄像机的这种算法。使用数字地形（或数字高程）地图（DTM/DEM）作为全局参考，可以恢复相机的绝对位置和方向。为了做到这一点，DTM被用来在两个连续帧中对应的特征之间制定一个约束。本文将这些约束条件扩展到处理非中心投影，就像许多全向系统的情况一样。结果表明，全方位数据的利用提高了导航算法的鲁棒性和精度。通过两种全向采集系统的实验验证了该算法的可行性。第一种是多折射相机，第二种是折反射相机。
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英文标题：
《Vision-Based Navigation III: Pose and Motion from Omnidirectional
  Optical Flow and a Digital Terrain Map》
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作者：
Ronen Lerner, Oleg Kupervasser and Ehud Rivlin
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最新提交年份：
2011
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Computer Vision and Pattern Recognition        计算机视觉与模式识别
分类描述：Covers image processing, computer vision, pattern recognition, and scene understanding. Roughly includes material in ACM Subject Classes I.2.10, I.4, and I.5.
涵盖图像处理、计算机视觉、模式识别和场景理解。大致包括ACM课程I.2.10、I.4和I.5中的材料。
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一级分类：Computer Science        计算机科学
二级分类：Artificial Intelligence        人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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英文摘要：
  An algorithm for pose and motion estimation using corresponding features in omnidirectional images and a digital terrain map is proposed. In previous paper, such algorithm for regular camera was considered. Using a Digital Terrain (or Digital Elevation) Map (DTM/DEM) as a global reference enables recovering the absolute position and orientation of the camera. In order to do this, the DTM is used to formulate a constraint between corresponding features in two consecutive frames. In this paper, these constraints are extended to handle non-central projection, as is the case with many omnidirectional systems. The utilization of omnidirectional data is shown to improve the robustness and accuracy of the navigation algorithm. The feasibility of this algorithm is established through lab experimentation with two kinds of omnidirectional acquisition systems. The first one is polydioptric cameras while the second is catadioptric camera.
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PDF链接：
https://arxiv.org/pdf/1106.6341