Prediction-based geometric feature extraction for 2D laser scanner

Authors:
Yilu Zhao;Xiong Chen
Affiliations:
-;-
Venue:
Robotics and Autonomous Systems
Year:
2011

Citing 6
Cited 3

Use of the Hough transformation to detect lines and curves in pictures

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A Split-and-Merge Segmentation Algorithm for Line Extraction in 2-D Range Images

ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 1
Line Extraction in 2D Range Images for Mobile Robotics

Journal of Intelligent and Robotic Systems
A comparison of line extraction algorithms using 2D range data for indoor mobile robotics

Autonomous Robots
Segmentation of Plane Curves

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Natural landmark extraction for mobile robot navigation based on an adaptive curvature estimation

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Poly line map extraction in sensor-based mobile robot navigation using a consecutive clustering algorithm

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Building maps of indoor environments by merging line segments extracted from registered laser range scans

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A transferable belief model applied to LIDAR perception for autonomous vehicles

Integrated Computer-Aided Engineering

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Abstract

This paper presents a novel algorithm for detecting line and circle features from 2D laser range scans. Unlike the conventional methods that use two stages for separating the features: data segmentation and feature separation in each segment, the proposed algorithm adopts a new structure and thus the computation complexity is much reduced. Moreover, it does not depend on prior knowledge of the environment, and it requires a minimum number of points per segment. We utilize prediction to achieve the above goals, so the algorithm is named prediction-based feature extraction (PFE). The efficiency and accuracy of the method is demonstrated by the experiments results.