Abstract
There are number of plane detection techniques for a given 3D point cloud utilized in different applications. All of the methods measure planes quality by computing sum of square error for a fitted plane model but no one of techniques may count the number of planes in the point cloud. In this chapter we present new strategy for validating number of found planes in the 3D:point cloud by applied cluster validity indices. For a planes finding in point cloud we have engaged the RANdom SAmple Consensus (RANSAC) method to synthetic and real scanned data. The experimental results have shown that the cluster validity indices may help in tuning RANSAC parameters as well as in determination the number of planes in 3D data.
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Acknowledgment
This research is funded by the European Social Fund under the project “Microsensors, microactuators and controllers for mechatronic systems (Go-Smart)” (Agreement No VP1-3.1-ŠMM-08-K-01-015).
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Lipnickas, A., Rimkus, K., Sinkevičius, S. (2014). Validation of Point Cloud Data for 3D Plane Detection. In: S. Hippe, Z., L. Kulikowski, J., Mroczek, T., Wtorek, J. (eds) Issues and Challenges in Artificial Intelligence. Studies in Computational Intelligence, vol 559. Springer, Cham. https://doi.org/10.1007/978-3-319-06883-1_3
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DOI: https://doi.org/10.1007/978-3-319-06883-1_3
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