Since their introduction by Kohonen Self Organizing Maps (SOMs) have been used in various forms for purposes of surface reconstruction. They offer robust and fast approximations of manifold data from unstructured input points while being modestly easy to implement. On the other hand SOMs have certain disadvantages when used in a setup where sparse, reliable and spacial unbounded data occurs. For example, airborne Lidar sensors generate a continuous stream of point data while flying above terrain. We introduce modifications of the SOM's data structure to adapt it to unbounded data. Furthermore, we introduce a new variation of the learning rule called rapid learning that is feasible for sparse but rather reliable data. We demonstrate examples where the surroundings of an aircraft can be reconstructed in almost real time.
Rapid self-organizing maps for terrain surface reconstruction
Enhanced and Synthetic Vision 2009 ; 2009 ; Orlando,Florida,United States
Proc. SPIE ; 7328
2009-04-30
Conference paper
Electronic Resource
English
Rapid self-organizing maps for terrain surface reconstruction [7328-06]
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