Visual Computing

University of Konstanz

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

F. Eisenkeil, T. Schafhitzel, U. Kühne, O. Deussen

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.

BibTeX

@proceedings{Eisenkeil2014Clusteringvisualizationnon,
  address    = {Bellingham},
  author     = {F. Eisenkeil and T. Schafhitzel and U. Kühne and O. Deussen},
  booktitle  = {Signal Processing, Sensor/Information Fusion, and Target Recognition XXIII ; 5–8 May 2014 Baltimore, Maryland, United States},
  doi        = {10.1117/12.2050497},
  editor     = {Kadar, Ivan},
  isbn       = {978-1-62841-028-0},
  note       = {Article Number: 90910V},
  number     = {9091},
  publisher  = {SPIE},
  series     = {Proceedings of SPIE},
  title      = {Clustering and visualization of non-classified points from LiDAR data for helicopter navigation},
  year       = {2014},
}

Supplemental Material

Paper (.pdf, 871.3 KB)