Line Segmentation of 2d Laser Scanner Point Clouds for Indoor Slam Based on a Range of Residuals

Abstract

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

Space Subdivision in Indoor Mobile Laser Scanning Point Clouds Based on Scanline Analysis

“…This segmentation approach first uses several points to fit a line and accepts it as a candidate if the mean values of a range of residuals is low enough. The following step combines the results of forward and backward processing to produce accurate linear segments and prevent tilted segments [ 29 ]. The points will be labelled as belonging to different segments after the segmentation process.…”

Automatic Indoor Reconstruction from Point Clouds in Multi-room Environments with Curved Walls

“…The image-based method for curved line detection often contains three steps, e.g., transformation to a binary image, image thinning and line detection. Some methods, such as the RANSAC [19,20], Hough transforms method [21] and least-square (LS) methods [22], directly detect straight lines in point clouds. LS methods are often used for line fitting or regular shape (e.g., circle, ellipse) fitting.…”

Design, Calibration, and Evaluation of a Backpack Indoor Mobile Mapping System

“…After segmenting the next scanline using a line segmentation procedure [29], a test on a distance threshold is used to decide whether a segment should be associated with an already reconstructed plane or be used to instantiate a new plane need. Currently, only horizontal and vertical planes are used.…”

“…The captured data pass through a series of processing steps before being subject to the registration's constraints. Firstly, the scanlines from each LRF are segmented by a line segmentation algorithm [29] and transformed to a frame system using the approximated parameters. Next, the all pairs of nearly co-planar line segments captured by two different LRFs are collected.…”