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Indoor mobile laser scanning (IMLS) based on the Simultaneous Localization and Mapping (SLAM) principle proves to be the preferred\udmethod to acquire data of indoor environments at a large scale. In previous work, we proposed a backpack IMLS system containing\udthree 2D laser scanners and an according SLAM approach. The feature-based SLAM approach solves all six degrees of freedom\udsimultaneously and builds on the association of lines to planes. Because of the iterative character of the SLAM process, the quality\udand reliability of the segmentation of linear segments in the scanlines plays a crucial role in the quality of the derived poses and\udconsequently the point clouds. The orientations of the lines resulting from the segmentation can be influenced negatively by narrow\udobjects which are nearly coplanar with walls (like e.g. doors) which will cause the line to be tilted if those objects are not detected as\udseparate segments. State-of-the-art methods from the robotics domain like Iterative End Point Fit and Line Tracking were found to not\udhandle such situations well. Thus, we describe a novel segmentation method based on the comparison of a range of residuals to a range\udof thresholds. For the definition of the thresholds we employ the fact that the expected value for the average of residuals of n points\udwith respect to the line is σ / √n. Our method, as shown by the experiments and the comparison to other methods, is able to deliver\udmore accurate results than the two approaches it was tested against

LINE SEGMENTATION OF 2D LASER SCANNER POINT CLOUDS FOR INDOOR SLAM BASED ON A RANGE OF RESIDUALS