Temporal integration of feature correspondences for enhanced recognition in cluttered and dynamic environments
Temporal integration of feature correspondences for enhanced recognition in cluttered and dynamic environments
复制标题
特征对应的时间整合，以增强杂乱和动态环境中的识别能力
DOI：
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发表时间：
2015
期刊：
影响因子：
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通讯作者：
M. Vincze
中科院分区：
文献类型：
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作者：
Thomas Faulhammer;A. Aldoma;M. Zillich;M. Vincze
我们提出了一种通过从多个观测点的关键点对应中积累低级信息来识别RGB-D点云中刚性对象实例的方法。与现有的多视图方法相比，我们对识别问题的假设更少，可以处理混乱和部分动态的环境，并且覆盖范围更广的对象。对公开可用的TUW和Willow数据集的评估表明，我们的方法在具有挑战性的静态环境序列中实现了最先进的识别性能，并且在观测过程中对部分变化的环境有了显着改善。
We propose a method for recognizing rigid object instances in RGB-D point clouds by accumulating low-level information from keypoint correspondences over multiple observations. Compared to existing multi-view approaches, we make fewer assumptions on the recognition problem, dealing with cluttered and partially dynamic environments as well as covering a wide range of objects. Evaluation on the publicly available TUW and Willow datasets showed that our method achieves state-of-the-art recognition performance for challenging sequences of static environments and a significant improvement for environments partially changing during the observation.
DOI：
10.1109/iros.2014.6942963
发表时间：
2014-09
期刊：
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影响因子：
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作者：
Lars Kunze;Christopher Burbridge;Marina Alberti;Akshaya Thippur;J. Folkesson;P. Jensfelt;Nick Hawes
通讯作者：
Lars Kunze;Christopher Burbridge;Marina Alberti;Akshaya Thippur;J. Folkesson;P. Jensfelt;Nick Hawes