期刊名称：ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
印刷版ISSN：2194-9042
电子版ISSN：2194-9050
出版年度：2013
卷号：XL-1/W1
页码：373-378
DOI：10.5194/isprsarchives-XL-1-W1-373-2013
出版社：Copernicus Publications
摘要：The automatic and accurate alignment of multiple point clouds is a basic requirement for an adequate digitization, reconstruction and interpretation of large 3D environments. Due to the recent technological advancements, modern devices are available which allow for simultaneously capturing intensity and range images with high update rates. Hence, such devices can even be used for dynamic scene analysis and for rapid mapping which is particularly required for environmental applications and disaster management, but unfortunately, they also reveal severe restrictions. Facing challenges with respect to noisy range measurements, a limited non-ambiguous range, a limited field of view and the occurrence of scene dynamics, the adequate alignment of captured point clouds has to satisfy additional constraints compared to the classical registration of terrestrial laser scanning (TLS) point clouds for describing static scenes. In this paper, we propose a new methodology for point cloud registration which considers such constraints while maintaining the fundamental properties of high accuracy and low computational effort without relying on a good initial alignment or human interaction. Exploiting 2D image features and 2D/2D correspondences, sparse point clouds of physically almost identical 3D points are derived. Subsequently, these point clouds are aligned with a fast procedure directly taking into account the reliability of the detected correspondences with respect to geometric and radiometric information. The proposed methodology is evaluated and its performance is demonstrated for data captured with a moving sensor platform which has been designed for monitoring from low altitudes. Due to the provided reliability and a fast processing scheme, the proposed methodology offers a high potential for dynamic scene capture and analysis.
关键词：LIDAR; Point Cloud; Registration; Imagery; Dynamic; Close Range
