ABSTRACT:The notable improvements on performance and low cost of digital cameras and GPS/IMU devices have caused MMSs (Mobile Mapping Systems) to be gradually becoming one of the most important devices for mapping highway and railway networks, generating and updating road navigation data and constructing urban 3D models over the last 20 years. Moreover, the demands for large scale visual street-level image database construction by the internet giants such as Google and Microsoft have made the further rapid development of this technology. As one of the most important sensors, the omni-directional cameras are being commonly utilized on many MMSs to collect panoramic images for 3D close range photogrammetry and fusion with 3D laser point clouds since these cameras could record much visual information of the real environment in one image at field view angle of 360° in longitude direction and 180° in latitude direction. This paper addresses the problem of panoramic epipolar image generation for 3D modelling and mapping by stereoscopic viewing. These panoramic images are captured with Point Grey's Ladybug3 mounted on the top of Mitsubishi MMS-X 220 at 2m intervals along the streets in urban environment. Onboard GPS/IMU, speedometer and post sequence image analysis technology such as bundle adjustment provided high accuracy position and attitude data for these panoramic images and laser data, this makes it possible to construct the epipolar geometric relationship between any two adjacent panoramic images and then the panoramic epipolar images could be generated. Three kinds of projection planes: sphere, cylinder and flat plane are selected as the epipolar images' planes. In final we select the flat plane and use its effective parts (middle parts of base line's two sides) for epipolar image generation. The corresponding geometric relations and results will be presented in this paper.
ABSTRACT:The aim of this paper is to introduce a novel technique of data integration between two different data sets, i.e. laser scanned RGB point cloud and oblique imageries derived 3D model, to create a 3D model with more details and better accuracy. In general, aerial imageries are used to create a 3D city model. Aerial imageries produce an overall decent 3D city models and generally suit to generate 3D model of building roof and some non-complex terrain. However, the automatically generated 3D model, from aerial imageries, generally suffers from the lack of accuracy in deriving the 3D model of road under the bridges, details under tree canopy, isolated trees, etc. Moreover, the automatically generated 3D model from aerial imageries also suffers from undulated road surfaces, non-conforming building shapes, loss of minute details like street furniture, etc. in many cases. On the other hand, laser scanned data and images taken from mobile vehicle platform can produce more detailed 3D road model, street furniture model, 3D model of details under bridge, etc. However, laser scanned data and images from mobile vehicle are not suitable to acquire detailed 3D model of tall buildings, roof tops, and so forth. Our proposed approach to integrate multi sensor data compensated each other's weakness and helped to create a very detailed 3D model with better accuracy. Moreover, the additional details li ke isolated trees, street furniture, etc. which were missing in the original 3D model derived from aerial imageries could also be integrated in the final model automatically. During the process, the noise in the laser scanned data for example people, vehicles etc. on the road were also automatically removed. Hence, even though the two dataset were acquired in different time period the integrated data set or the final 3D model was generally noise free and without unnecessary details.
Castellated walls are positioned as tangible cultural heritage, which require regular maintenance to preserve their original state. For the demolition and repair work of the castellated wall, it is necessary to identify the individual stones constituting the wall. However, conventional approaches using laser scanning or integrated circuits (IC) tags were very time-consuming and cumbersome. Therefore, we herein propose an efficient approach for castellated wall management based on an extended multiscale image segmentation technique. In this approach, individual stone polygons are extracted from the castellated wall image and are associated with a stone management database. First, to improve the performance of the extraction of individual stone polygons having a convex shape, we developed a new shape criterion named <i>convex hull fitness</i> in the image segmentation process and confirmed its effectiveness. Next, we discussed the stone management database and its beneficial utilization in the repair work of castellated walls. Subsequently, we proposed irregular-shape indexes that are helpful for evaluating the stone shape and the stability of the stone arrangement state in castellated walls. Finally, we demonstrated an application of the proposed method for a typical castellated wall in Japan. Consequently, we confirmed that the stone polygons can be extracted with an acceptable level. Further, the condition of the shapes and the layout of the stones could be visually judged with the proposed irregular-shape indexes.
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