Line extraction in 2D range images for mobile robotics
Line extraction in 2D range images for mobile robotics
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DOI：
10.1023/b:jint.0000038945.55712.65
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发表时间：
2004-07-01
影响因子：
3.3
通讯作者：
Aldon, MJ
中科院分区：
文献类型：
--
作者：
Borges, GA;Aldon, MJ
本文提出了一种几何特征检测框架，用于传统的二维激光测距仪。该框架由数据预处理、断点检测和直线提取三个主要步骤组成。在数据预处理中，讨论了底层数据的组织和处理，重点讨论了传感器偏差补偿。断点检测允许确定不被扫描表面变化中断的测量序列。两个断点检测器进行了研究，一个基于自适应阈值，另一个基于卡尔曼滤波。这两个检测器的实现和调谐也进行了研究。应用线核对距离图像中的每个连续扫描序列进行线提取。我们已经研究了两个经典的内核，通常用于移动的机器人，和我们的分裂和合并模糊（SMF）线提取器。SMF采用模糊聚类的分裂和合并的框架，而不需要猜测集群的数量。使用模拟和真实的图像的定性和定量比较说明了当使用不同的断点和线检测方法时框架的主要特征。这些比较说明了每个估计器的特性，可以根据平台的计算能力和应用程序的精度要求来利用这些特性。
This paper presents a geometrical feature detection framework for use with conventional 2D laser rangefinders. This framework is composed of three main procedures: data pre-processing, breakpoint detection and line extraction. In data pre-processing, low-level data organization and processing are discussed, with emphasis to sensor bias compensation. Breakpoint detection allows to determine sequences of measurements which are not interrupted by scanning surface changing. Two breakpoint detectors are investigated, one based on adaptive thresholding, and the other on Kalman filtering. Implementation and tuning of both detectors are also investigated. Line extraction is performed to each continuous scan sequence in a range image by applying line kernels. We have investigated two classic kernels, commonly used in mobile robots, and our Split-and-Merge Fuzzy (SMF) line extractor. SMF employs fuzzy clustering in a split-and-merge framework without the need to guess the number of clusters. Qualitative and quantitative comparisons using simulated and real images illustrate the main characteristics of the framework when using different methods for breakpoint and line detection. These comparisons illustrate the characteristics of each estimator, which can be exploited according to the platform computing power and the application accuracy requirements.