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Outlier detection for scanned point clouds using majority voting |
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| Affiliation: | 1. State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China;2. The Robotics Institute, Carnegie Mellon University, 5000 Forbes Ave, Pittsburgh, PA 15213, USA;3. Geography and Remote Sensing, Nanjing University of Information Science and Technology, Nanjing 210044, China;1. Department of Pharmacy, Wuhan University, Wuhan 430072, China;2. School of Pharmacy, Hubei University of Science and Technology, Xianning 437100, China;1. School of Mathematical Sciences, Dalian University of Technology, Dalian, China;2. School of Mathematics and Information Science, Nanchang Hangkong University, Nanchang, China;3. School of Mathematical Sciences, University of Science and Technology of China, Hefei, China;1. Fujian Key Laboratory of Sensing and Computing for Smart Cities, Department of Computer Science, Xiamen University, Xiamen, FJ 361005, China;2. School of Software, Xiamen University, Xiamen, FJ 361005, China;3. Department of Geography & Environmental Management, University of Waterloo, Waterloo, ON N2L 3G1, Canada |
| Abstract: | When scanning an object using a 3D laser scanner, the collected scanned point cloud is usually contaminated by numerous measurement outliers. These outliers can be sparse outliers, isolated or non-isolated outlier clusters. The non-isolated outlier clusters pose a great challenge to the development of an automatic outlier detection method since such outliers are attached to the scanned data points from the object surface and difficult to be distinguished from these valid surface measurement points. This paper presents an effective outlier detection method based on the principle of majority voting. The method is able to detect non-isolated outlier clusters as well as the other types of outliers in a scanned point cloud. The key component is a majority voting scheme that can cut the connection between non-isolated outlier clusters and the scanned surface so that non-isolated outliers become isolated. An expandable boundary criterion is also proposed to remove isolated outliers and preserve valid point clusters more reliably than a simple cluster size threshold. The effectiveness of the proposed method has been validated by comparing with several existing methods using a variety of scanned point clouds. |
| Keywords: | 3D Laser scanning Point cloud Outlier detection Non-isolated outliers Majority voting Feature preservation |
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