Integration of multiple partial point clouds based on estimated parameters in photogrammetry with QR codes

馬場 啓多*; 渡部 有隆*; 中村 啓太*; 松本 拓  ; 羽成 敏秀 ; 川端 邦明   

Baba, Keita*; Watanobe, Yutaka*; Nakamura, Keita*; Matsumoto, Taku; Hanari, Toshihide; Kawabata, Kuniaki

This study proposes a partial-to-partial point cloud registration method based on estimated parameters in photogrammetry and QR code. Some research and development on Generating a 3D map of the workspace by photogrammetric methods have been proposed for the decommissioning work at the Fukushima Daiichi Nuclear Power Plant. Photogrammetry is a method for 3D reconstruction of the location and shape of target objects from many images, and the processing time depends on the number of images. Considering the reconstruction of a large area, the number of images increases, and processing time also increases significantly. To reduce such computational time, this study considers applying SfM-MVS (Structure from Motion and Multi-View Stereo), which is one of the photogrammetry methods, to each segmented image group, aligning each obtained result, integrating them, and creating a model of the entire space. This alignment is called partial-to-partial registration and it is difficult to find the correspondence points for registration. Therefore, we place markers such as QR codes in the target reconstruction space to make it easy to find the correspondence points. We adopt the QR code as a 2D code because it is easy to reconstruct by photogrammetry. In this paper, we discuss the validity of this approach by comparing it with the integrated model using all images applying SfM-MVS. We verify the validation of the proposed method by simulation due to the large number of images and the ease of modifying the environment. The experiment about varying the number of image divisions shows that the reconstruction result from all images is more accurate than the integrated result. However, all of these models have high reconstruction accuracy. Moreover, the accuracy of the integrated model does not depend on the number of divisions.