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ALGORITHM FOR INCREASING THE SPATIAL DENSITY OF A LIDAR POINT CLOUD FOR SOLVING PROBLEMS OF AUTONOMOUS DRIVING

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Several algorithms are proposed to solve the problem of increasing the density of points of lidar clouds, including the algorithms based on neural networks. High density lidar clouds can improve the accuracy of scene recognition and localization algorithms. The algorithms quality is assessed by the following metrics: the number of false points, the average error, the root-mean-square error. In contrast to existing ones, the proposed algorithms are aimed at determining not only additional points positions, but also their reflectivity coefficients. Presented results of experiments demonstrate that the neural network algorithm without the use of normalization and sigmoidal weighting of the loss function has the smallest error. The least number of false points is provided by the neural network algorithm with an added normalization.

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