3-D Object Recognition of a Robotic Navigation Aid for the Visually Impaired.
3-D Object Recognition of a Robotic Navigation Aid for the Visually Impaired.
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DOI：
10.1109/tnsre.2017.2748419
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发表时间：
2018-03
期刊：
影响因子：
--
通讯作者：
Qian X
中科院分区：
文献类型：
--
作者：
Ye C;Qian X
本文提出了一种3D物体识别方法及其在机器人导航辅助设备（RNA）上的实现，以允许盲人导航的室内结构物体的实时检测。该方法将点云数据分割成多个平面面片，并提取它们的面间关系。该方法在已有的对象模型知识产权基础上，定义了6个高层次特征，并确定了每个面片的高层次特征。然后设计了一个基于高斯混合模型的平面分类器，将每个平面片分类为属于特定对象模型的一个。最后，一个递归的平面聚类过程是用来聚类的分类平面到模型对象。由于该方法使用几何上下文来检测目标，因此对目标的视觉外观变化具有鲁棒性。因此，它非常适合于检测结构对象（例如，楼梯、门口等）。此外，它还具有很高的可扩展性和并行性。该方法也能够检测一些室内非结构性物体。实验结果表明，该方法具有较高的目标识别成功率。
This paper presents a 3D object recognition method and its implementation on a Robotic Navigation Aid (RNA) to allow real-time detection of indoor structural objects for the navigation of a blind person. The method segments a point cloud into numerous planar patches and extracts their Inter-Plane Relationships (IPRs). Based on the existing IPRs of the object models, the method defines 6 High Level Features (HLFs) and determines the HLFs for each patch. A Gaussian-Mixture-Model-based plane classifier is then devised to classify each planar patch into one belonging to a particular object model. Finally, a recursive plane clustering procedure is used to cluster the classified planes into the model objects. As the proposed method uses geometric context to detect an object, it is robust to the object’s visual appearance change. As a result, it is ideal for detecting structural objects (e.g., stairways, doorways, etc.). In addition, it has high scalability and parallelism. The method is also capable of detecting some indoor non-structural objects. Experimental results demonstrate that the proposed method has a high success rate in object recognition.