Håkan Almqvist, Martin Magnusson, Tomasz Piotr Kucner, and Achim J. Lilienthal
Learning to detect misaligned point clouds
to appear in Journal of Field Robotics
To match and merge several overlapping point clouds into larger point clouds is a common procedure in many applications such as mobile robotics, 3D-mapping and object visualization. However, fully automatic point cloud merging, without manual verification, is still not possible, since no matching algorithms of today provide any certain methods for detecting misaligned point clouds. In this article we make a comparative evaluation of geometric consistency methods for classifying aligned and nonaligned point cloud pairs. We also propose a method that combines the result of the evaluated methods to further improve the classification of the point clouds. We compare a range of methods on two data sets from different environments related to mobile robotics and mapping. The results show that methods based on a Normal Distributions Transform representation of the point clouds perform best under the circumstances presented herein. We also show that the performance of the combined method are equal to the best tested methods.