In this paper a new triangulation‐based laser scanner is presented which has a simple, yet strong, flexible and low‐cost structure. A digital camera and three laser line projectors are the main components of the system. One of the laser projectors is positioned vertically, while the other two are horizontal. The former scans the object, whereas the latter two establish an optical frame which is used, in part, to define the plane containing the vertical laser projector at each step of scanning. At each step, an image is taken which includes the object along with the projected laser lines. By intersecting the vertical and horizontal lines a couple of points are formed which, along with the calibration information of the system, enable the extraction of the object coordinates. Results of the tests carried out show that by using an optical frame of this nature, the process of scanning is greatly facilitated. That is, the scanner can easily be used to scan objects of different size and dimensions. Within the current configuration, the system enables measurements with an accuracy of 1/1600. Also, as the system has a rigorous basis, its accuracy can be increased if improved hardware is provided.

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This paper proposes a new fringe projection scanner that uses a dual pattern to prevent phase retrieval errors or complications. The pattern comprises two horizontal and vertical patterns, however, just one of them is involved in computing the coordinates of an object point. To define the pattern, a photometric stereo component is used to extract the geometric properties of the object. The extracted data is employed to form a decision mask, each pixel of which defines the best pattern (horizontal, vertical or both) related to one of the object points. In this way, the scanner determines which pattern to use to obtain the most accurate results. Experiments suggest not only the avoidance of phase retrieval problems, but also an increase in the accuracy of the measurements (by 56% in this test).

Abstract. In recent years, the applications of interior and exterior model of buildings have been increased in the field of surveying and mapping. This paper presents a new method for extracting a two-dimensional (2D) floor plan of a building from Simultaneous localization and mapping (SLAM)-based point clouds. In the proposed algorithm, after preprocessing, the voxel space is generated for the point cloud. Then, the optimal section of the voxel cube to generate building floor plan is identified. Finally, the linear structures and walls are extracted using the random sample consensus (RANSAC) algorithm. The proposed algorithm was examined on a collected point clouds of a building, and the walls of this building were automatically extracted. To evaluate the proposed method, the obtained walls by the algorithm were compared with the manually extracted walls. The algorithm successfully extracted almost 90% of the walls in the test area. Moreover, the average error of 3 cm for the extracted walls proved the high accuracy of the proposed method for building floor plan modeling.

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