Unsupervised learning of spatial-temporal models of objects in a long-term autonomy scenario
Unsupervised learning of spatial-temporal models of objects in a long-term autonomy scenario
复制标题
长期自治场景中对象时空模型的无监督学习
DOI：
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发表时间：
2015
期刊：
影响因子：
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通讯作者：
P. Jensfelt
中科院分区：
文献类型：
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作者：
Rares Ambrus;Johan Ekekrantz;J. Folkesson;P. Jensfelt
我们提出了一种新颖的方法，通过对室内 RGB-D 场景的时空分布进行分析，对重复观察的室内 RGB-D 场景的分段动态部分进行聚类。我们使用场景差异来分割场景中感兴趣的区域以进行变化检测。我们扩展了 Meta-Room 方法，并评估了移动机器人在 30 天内自主获取的复杂数据集的性能。我们使用初始聚类方法根据外观和形状对分割部分进行分组，并进一步组合通过分析其时空行为获得的聚类。我们表明，使用时空信息进一步提高了匹配精度。
We present a novel method for clustering segmented dynamic parts of indoor RGB-D scenes across repeated observations by performing an analysis of their spatial-temporal distributions. We segment areas of interest in the scene using scene differencing for change detection. We extend the Meta-Room method and evaluate the performance on a complex dataset acquired autonomously by a mobile robot over a period of 30 days. We use an initial clustering method to group the segmented parts based on appearance and shape, and we further combine the clusters we obtain by analyzing their spatial-temporal behaviors. We show that using the spatial-temporal information further increases the matching accuracy.