Clustering and visualization of non-classified points from LiDAR data for helicopter navigation

Ferdinand Eisenkeil; Tobias Schafhitzel; Uwe Kühne; Oliver Deussen

doi:10.1117/12.2050497

20 June 2014 Clustering and visualization of non-classified points from LiDAR data for helicopter navigation

Ferdinand Eisenkeil, Tobias Schafhitzel, Uwe Kühne, Oliver Deussen

Proceedings Volume 9091, Signal Processing, Sensor/Information Fusion, and Target Recognition XXIII; 90910V (2014) https://doi.org/10.1117/12.2050497
Event: SPIE Defense + Security, 2014, Baltimore, MD, United States

Abstract

In this paper we propose a dynamic DBSCAN-based method to cluster and visualize unclassified and potential dangerous obstacles in data sets recorded by a LiDAR sensor. The sensor delivers data sets in a short time interval, so a spatial superposition of multiple data sets is created. We use this superposition to create clusters incrementally. Knowledge about the position and size of each cluster is used to fuse clusters and the stabilization of clusters within multiple time frames. Cluster stability is a key feature to provide a smooth and un-distracting visualization for the pilot. Only a few lines are indicating the position of threatening unclassified points, where a hazardous situation for the helicopter could happen, if it comes too close. Clustering and visualization form a part of an entire synthetic vision processing chain, in which the LiDAR points support the generation of a real-time synthetic view of the environment.

Citation Download Citation

Ferdinand Eisenkeil, Tobias Schafhitzel, Uwe Kühne, and Oliver Deussen "Clustering and visualization of non-classified points from LiDAR data for helicopter navigation", Proc. SPIE 9091, Signal Processing, Sensor/Information Fusion, and Target Recognition XXIII, 90910V (20 June 2014); https://doi.org/10.1117/12.2050497

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