This paper presents an original technique for robust detection of line features from range data, which is also the core element of\nan algorithm conceived for mapping 2D environments. A new approach is also discussed to improve the accuracy of position and\nattitude estimates of the localization by feeding back angular information extracted from the detected edges in the updating map.\nThe innovative aspects of the line detection algorithm regard the proposed hierarchical clusterization method for segmentation.\nInstead, line fitting is carried out by exploiting the Principal Component Analysis, unlike traditional techniques relying on least\nsquares linear regression. Numerical simulations are purposely conceived to compare these approaches for line fitting. Results\ndemonstrate the applicability of the proposed technique as it provides comparable performance in terms of computational load\nand accuracy compared to the least squares method. Also, performance of the overall line detection architecture, as well as of\nthe solutions proposed for line-based mapping and localization-aiding, is evaluated exploiting real range data acquired in indoor\nenvironments using an UTM-30LX-EW 2D LIDAR. This paper lies in the framework of autonomous navigation of unmanned\nvehicles moving in complex 2D areas, for example, being unexplored, full of obstacles, GPS-challenging, or denied.
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