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We have presented a new method for computing 3D models from unorganized raw 3D data Since our method works with raw 3D point sets we do not need to know anything about the kind of sensor used for obtaining dataThe method proposed here can be used with most of the 3D scanner devices First we have explained an algorithm for computing the planar patches that fits with the planar surfaces in the 3D scene This is a low complexity method that can be used for obtaining online 3D models We have also used a GNG model in order to increase the level of detail for the resulting modelsResults have been shown for both time-of-flight cameras and 3D range laser The usefulness of our models is demonstrated by applying an 6DoF egomotion algorithm that uses those models as input for computations It have been proved that the use of GNG improves 6Dof mapping results making it possible to use this approach with any kind of 3D sensorThe proposed modification of GNG to represent 3D data sequences accelerates the learning algorithm and allows the architecture to work faster This is possible because the system does not restart the map for each frame in the sequence only readjust the network structure starting from previous map without inserting or deleting neurons Experiments show that the proposed method can be applied to the problem of automatic map reconstruction The results show significant improvements over the classical solutions The use of GNG to improve the feature extraction process has been a key aspect in improving 6DoF egomotion computationAs future work we plan to improve the accuracy and performance of our method in order to use it in 6D SLAM
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