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In this paper, we propose a method for automated mosaicking of multiple 3D point clouds generated from a depth camera. A depth\udcamera generates depth data by using ToF (Time of Flight) method and intensity data by using intensity of returned signal. The depth\udcamera used in this paper was a SR4000 from MESA Imaging. This camera generates a depth map and intensity map of 176 x 44\udpixels. Generated depth map saves physical depth data with mm of precision. Generated intensity map contains texture data with\udmany noises. We used texture maps for extracting tiepoints and depth maps for assigning z coordinates to tiepoints and point cloud\udmosaicking. There are four steps in the proposed mosaicking method. In the first step, we acquired multiple 3D point clouds by\udrotating depth camera and capturing data per rotation. In the second step, we estimated 3D-3D transformation relationships between\udsubsequent point clouds. For this, 2D tiepoints were extracted automatically from the corresponding two intensity maps. They were\udconverted into 3D tiepoints using depth maps. We used a 3D similarity transformation model for estimating the 3D-3D\udtransformation relationships. In the third step, we converted local 3D-3D transformations into a global transformation for all point\udclouds with respect to a reference one. In the last step, the extent of single depth map mosaic was calculated and depth values per\udmosaic pixel were determined by a ray tracing method. For experiments, 8 depth maps and intensity maps were used. After the four\udsteps, an output mosaicked depth map of 454x144 was generated. It is expected that the proposed method would be useful for\uddeveloping an effective 3D indoor mapping method in future

the international archives of the photogrammetry, remote sensing and spatial information sciences/international archives of the photogrammetry, remote sensing and spatial information sciences

AUTOMATED MOSAICKING OF MULTIPLE 3D POINT CLOUDS GENERATED FROM A DEPTH CAMERA