Geometric feature extraction from 2D laser range data for mobile robot navigation

Feature extraction and segmentation obviously play an utmost important role in autonomous mobile robot localization and navigation. In this paper we discuss some line segmentation and feature extraction algorithms and proposed an adaptive feature extraction algorithm for 2D laser range data. Features in indoor environments that are considered in this paper can be described as two geometric primitives: line segments which represent the walls, and corners. Segmentation process estimates the line segments belong to the walls and which represent the same object have been grouped together. Intersecting points of the line segments, called corners are treated as landmarks. Some segmentation algorithms are implemented and tested using laser range data captured in different environments and the effectiveness of them is analyzed. Finally, a better adaptive feature extraction and segmentation algorithm which generates a line map of an unstructured environment is proposed.