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论文类型：期刊论文

发表时间：2011-10-01

发表刊物：Zidonghua Xuebao/Acta Automatica Sinica

收录刊物：EI、PKU、ISTIC、Scopus

卷号：37

期号：10

页面范围：1232-1240

ISSN号：02544156

摘要：This paper mainly studies the indoor scene cognition problem for mobile robots. The structure of an indoor scene is assumed to be structured, while indoor objects are in a variety of forms, which are difficult to be represented by specific descriptive models. In our work, planes are extracted from 3D laser data using regional expansion algorithm, and the properties as well as the relationship of these planes are used for the indoor structure identification. In order to adopt digital image processing algorithm to implement object detection, a bearing angle model is used to represent laser point clouds, so that 3D laser scanning data can be converted to 2D bearing angle image. It is difficult to detect a large number of different classes of objects in cluttered indoor scenes, especially when the mobile robot acquires the 3D laser scanning data in different locations and angles of view. An approach based on gentleboost algorithm is proposed for multi-class object detection, which takes the fragments and the location with respect to the object center as the generic features for object detection. As the result of indoor structure identification, the specific region for ceiling, floor, wall and door can be labeled in the bearing angle image. With the help of known semantic information, the false object detection results can be eliminated effectively. Experiment results implemented on a real mobile robot show the validity of the proposed method. Copyright ? 2011 Acta Automatica Sinica. All rights reserved.