A probabilistic approach for semantic classification using laser range data in indoor environments

B. Kaleci, Cagri Mete Senler, H. Dutagaci, O. Parlaktuna
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引用次数: 9

Abstract

In this paper, a probabilistic approach is proposed for semantic classification in indoor environments using laser range data. Robot locations in indoor environments are categorized into three broad classes as room, corridor, and door. K-means and Learning Vector Quantization (LVQ) methods are used to classify robot positions. Circular shifting is applied to render laser range data independent of robot pose. K-means or LVQ algorithms are used to determine data clusters and their centers. In K-means method, the cluster centers are modelled with the proposed probabilistic approach to consider the semantic class of robot location. On the other hand, LVQ method inherently provides semantic classes of the cluster centers. In order to improve the rate of classification success, Markov model is integrated into the proposed approach. Experiments are conducted to demonstrate the effectiveness of the proposed approach. The results indicate that K-means method successfully classifies rooms and corridors, but door classification success rate is not satisfactory. LVQ method improves door classification rate without decreasing the classification rate of corridor and room. Lastly, effectiveness of the Markov model is discussed.
一种基于室内激光距离数据的概率语义分类方法
本文提出了一种利用激光距离数据进行室内环境语义分类的概率方法。机器人在室内环境中的位置分为三大类:房间、走廊和门。使用K-means和学习向量量化(LVQ)方法对机器人位置进行分类。采用圆移位技术,使激光距离数据与机器人姿态无关。使用K-means或LVQ算法来确定数据簇及其中心。在K-means方法中,采用提出的概率方法对聚类中心进行建模,以考虑机器人定位的语义类别。另一方面,LVQ方法固有地提供了聚类中心的语义类。为了提高分类成功率,将马尔可夫模型集成到该方法中。实验证明了该方法的有效性。结果表明,K-means方法对房间和走廊的分类成功率较高,但对门的分类成功率不理想。LVQ方法在不降低走廊和房间分类率的前提下,提高了门的分类率。最后,讨论了马尔可夫模型的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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