Cleaning massive sonar point clouds

We consider the problem of automatically cleaning massive sonar data point clouds, that is, the problem of automatically removing noisy points that for example appear as a result of scans of (shoals of) fish, multiple reflections, scanner self-reflections, refraction in gas bubbles, and so on. We describe a new algorithm that avoids the problems of previous local-neighbourhood based algorithms. Our algorithm is theoretically I/O-efficient, that is, it is capable of efficiently processing massive sonar point clouds that do not fit in internal memory but must reside on disk. The algorithm is also relatively simple and thus practically efficient, partly due to the development of a new simple algorithm for computing the connected components of a graph embedded in the plane. A version of our cleaning algorithm has already been incorporated in a commercial product. Categories and Subject Descriptors: F.2.2 [Analysis of algorithms and problem complexity]: Nonnumerical algorithms and problems...

• Type: Journal
• Year: 2010
• Where: GIS
• Authors: Lars Arge, Kasper Green Larsen, Thomas Mølhave, Freek van Walderveen
• Keywords: Algorithms, Automated Reasoning, GIS 2010, Massive Sonar, Massive Sonar Data