Fast object detection for robots in a cluttered indoor environment using integral 3D feature table
Fast object detection for robots in a cluttered indoor environment using integral 3D feature table
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DOI：
10.1109/icra.2011.5980129
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发表时间：
2011-05
期刊：
影响因子：
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通讯作者：
Asako Kanezaki;Takahiro Suzuki;T. Harada;Y. Kuniyoshi
中科院分区：
文献类型：
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作者：
Asako Kanezaki;Takahiro Suzuki;T. Harada;Y. Kuniyoshi
实现机器人在室内环境中的自动目标搜索是移动的机器人研究中最重要且最具挑战性的课题之一。如果目标物体不存在于附近区域，那么显而易见的策略是去最后观察到它的区域。我们已经开发了一个机器人系统，在自动例行爬行过程中收集室内环境中的3D场景数据，并通过对收集到的3D场景数据进行全局搜索来快速检测对象。通过利用自定位信息将彩色图像和距离图像转换为一组彩色体素数据，可以自动获得三维场景数据。为了检测对象，系统将目标对象的边界框在颜色体素数据中移动一定的步长，提取每个框区域中的3D特征，并使用预先学习的适当特征投影来计算这些特征与目标对象的特征之间的相似性。利用我们的3D特征的加性，特征提取和相似性计算都大大加快。在对象学习过程中，系统通过对目标对象的独特特征而不是其共同特征进行加权来获得特征投影矩阵，从而减少了对象检测错误。
Realizing automatic object search by robots in an indoor environment is one of the most important and challenging topics in mobile robot research. If the target object does not exist in a nearby area, the obvious strategy is to go to the area in which it was last observed. We have developed a robot system that collects 3D-scene data in an indoor environment during automatic routine crawling, and also detects objects quickly through a global search of the collected 3D-scene data. The 3D-scene data can be obtained automatically by transforming color images and range images into a set of color voxel data using self-location information. To detect an object, the system moves the bounding box of the target object by a certain step in the color voxel data, extracts 3D features in each box region, and computes the similarity between these features and the target object's features, using an appropriate feature projection learned beforehand. Taking advantage of the additive property of our 3D features, both feature extraction and similarity calculation are considerably accelerated. In the object learning process, the system obtains the feature-projection matrix by weighting unique features of the target object rather than its common features, resulting in reducing object detection errors.