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Using GNG to improve 3D feature extraction—Application to 6DoF egomotion
2012
Neural Networks
Several recent works deal with 3D data in mobile robotic problems, e.g. mapping or egomotion. Data comes from any kind of sensor such as stereo vision systems, time of flight cameras or 3D lasers, providing a huge amount of unorganized 3D data. In this paper, we describe an efficient method to build complete 3D models from a Growing Neural Gas (GNG). The GNG is applied to the 3D raw data and it reduces both the subjacent error and the number of points, keeping the topology of the 3D data. The
doi:10.1016/j.neunet.2012.02.014
pmid:22386789
fatcat:ou7fcbtyx5fh3no25nnkzghlbq