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Geo-registration of wide-baseline panoramic image sequences using a digital map reference

机译:使用数字地图参考对宽基线全景图像序列进行地理配准

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This paper describes a method of geo-registering a sequence of panoramic images to a digital map by matching pixel information from the images with information on the building footprint contained in a digital map. Recently, images captured at the ground level using a Mobile Mapping System (MMS), such as the panoramic images displayed by Google Street View, have been considered as a valuable resource for three-dimensional (3D) building modeling. However, the wide intervals between these panoramic images, as well as locational and directional error from the related sensors, make it difficult to analyze the image data. This paper demonstrates a formulation method for connecting pixels in panoramic images with information on footprint vertices and building lines contained in a digital map. To allow both pixel and footprint information consistent in 3D space, each panoramic image is tilt-corrected in pre-processing to upright the image using the estimated pitch and roll of a vehicle and removing the pitch and roll effects from the panoramic image pixels. Through the proposed formulation, a single panoramic image can be easily geo-registered with simple user-provided constraints, and adjacent sequential images can then be automatically geo-registered using point feature matching. Experimental results showed a significant reduction in the locational and directional error of sequential panoramic images, and the proposed vanishing point (VP) based validation process was found to successfully detect failure cases.
机译:本文介绍了一种方法,通过将全景图像序列中的像素信息与数字地图中包含的建筑物占地面积信息进行匹配,将全景图像序列地理注册到数字地图中。最近,使用移动制图系统(MMS)在地面上捕获的图像(例如Google Street View显示的全景图像)已被视为三维(3D)建筑建模的宝贵资源。然而,这些全景图像之间的宽间隔以及来自相关传感器的位置和方向误差使得难以分析图像数据。本文演示了一种将全景图像中的像素与足迹顶点和数字地图中包含的构建线信息连接起来的公式化方法。为了使像素信息和足迹信息在3D空间中保持一致,在预处理过程中对每个全景图像进行倾斜校正,以使用估计的车辆俯仰和横滚直立图像,并从全景图像像素中消除俯仰和横滚效果。通过提出的公式,可以轻松地通过用户提供的简单约束对单个全景图像进行地理配准,然后可以使用点特征匹配对相邻的顺序图像进行自动地理配准。实验结果表明,连续全景图像的位置和方向误差显着减少,并且发现基于提议的消失点(VP)的验证过程可以成功检测出故障案例。

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