Unsupervised 3D Object Discovery and Categorization for Mobile Robots

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• 刊名：Springer Tracts in Advanced Robotics
• 出版年：2017
• 出版时间：2017
• 年：2017
• 卷：100
• 期：1
• 页码：61-76
• 全文大小：427 KB
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• 作者单位：Jiwon Shin (5)
  Rudolph Triebel (6)
  Roland Siegwart (5)
  
  5. Autonomous Systems Lab, ETH Zurich, Zurich, Switzerland
  6. The Oxford Mobile Robotics Group, University of Oxford, Oxford, England
  
• 丛书名：Robotics Research
• ISBN：978-3-319-29363-9
• 刊物类别：Engineering
• 刊物主题：Automation and Robotics
  Control Engineering
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1610-742X
• 卷排序：100

文摘

We present a method for mobile robots to learn the concept of objects and categorize them without supervision using 3D point clouds from a laser scanner as input. In particular, we address the challenges of categorizing objects discovered in different scans without knowing the number of categories. The underlying object discovery algorithm finds objects per scan and gives them locally-consistent labels. To associate these object labels across all scans, we introduce class graph which encodes the relationship among local object class labels. Our algorithm finds the mapping from local class labels to global category labels by inferring on this graph and uses this mapping to assign the final category label to the discovered objects. We demonstrate on real data our algorithm’s ability to discover and categorize objects without supervision.