基于三角测量和霍夫变换的移动机器人声纳数据融合

Liu Baolong, He Bo, W. Yongqing, Zhao Xuan, Guo Lei
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引用次数: 2

摘要

宝丽来声纳传感器仍然是移动机器人中应用最广泛的传感器之一。本文提出了一种新的传感器融合方案THF (triangulation Hough fusion),它结合了基于三角剖分的融合算法和抑制聚类的Hough变换算法的优点。THF的思想在于对提取的点进行改进的霍夫变换,而不是直接对原始声纳数据进行变换。将THF算法应用于聚类和噪声声纳数据,可以可靠地检测环境的点和线段特征。我们通过在模拟室内环境中提取几何特征来证明THF算法的有效性,其中移动机器人与随意走动的行人一起移动。实验表明,THF是一种有效的点特征和线段特征检测方法,特别是在结构化环境中。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Triangulation & Hough Transform Based Fusion of Sonar Data for Mobile Robotics
Polaroid sonar sensors are still one of the most widely used sensors in mobile robotics. In this paper a novel sensor fusion scheme called THF (triangulation Hough fusion), which combines the advantages of TBF (triangulation based fusion) and CIHT (cluster inhibiting Hough transform), is presented. The idea of THF lies in that a modified Hough transform is employed on the points extracted by TBF, not on the raw sonar data directly. THF algorithm applied on clustered and noisy sonar data could reliably detect both point and line segment features of the environment. We demonstrate the effectivity of THF algorithm by extracting geometric features in a simulated indoor environment, in which a mobile robot travels with pedestrians moving around casually. Experiments demonstrate that THF is an effective and efficient scheme for detecting both point features and line segment features, especially in the structured environment.
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