Two-dimensional laser feature extraction based on improved successive edge following

Abstract

Successive edge following (SEF) has been widely used to describe the environmental characteristics based on a two-dimensional (2D) laser range finder due to its simplicity. However, the segmentation accuracy of the regular SEF for different distances is very low. And besides that, the regular SEF sometimes fails to characterize the corner features in the continuous segmentation. To solve these problems, we propose an improved SEF approach, which combines 2D polar radius-arc, adaptive threshold in a region to divide different radius data into groups more reasonably. Region growing is also discussed to localize the corner based on variance of segmentation and its fitting. Finally, all features described by line segments are used to build the map of the environment. As compared to the regular SEF, our approach is information-lossless. Our experimental results show that the segmentation accuracy is improved, while the segmentation number is more realistic and the feature detection accuracy is increased, as compared with the regular SEF.

Multidimensional Nonlinear Landscape Planning Based on Parameter Feature Extraction and Multimedia Technology

“…Information technology crosses national boundaries and nations, making all corners of the world closely connected due to the development of information network. In the information age, a market model and enterprise model di erent from that in the industrial age have been formed [3,4]. Mankind began to transform from industrial civilization to information civilization.…”

Adaptive Obstacle Detection for Mobile Robots in Urban Environments Using Downward-Looking 2D LiDAR

“…Sarkar et al proposed an offline method to build maps of indoor environments by using line segments extracted from laser range data [ 32 ]. Several algorithms had been proposed for extracting line segments from 2D LiDAR data, Improved Successive Edge Following algorithm [ 33 ], Recursive Line Extraction algorithm [ 34 ] and qualitative and quantitative comparisons had been applied using different methods include Line Tracking, Iterative End-Point Fit (IEPF) and Split and Merge Fuzzy algorithms [ 35 ], IEPF is used in this paper, because it is simple and efficient. In these algorithms, obstacles were referred to objects above the road surface.…”