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Scene registration of 3D laser rangefinder scans is increasingly being required in applications, such as mobile robotics, that demand a timely response. For speeding up point matching methods, the large amount of range data should be reduced. This sampling, in turn, can have a significant impact on accuracy. In particular, genetic algorithms provide a robust optimization method that avoids local minima for scan matching, but their computational cost grows with the number of points. This paper proposes a new point sampling strategy that considers the spherical scanning process of most sensors to equalize the measure-direction density. This fast sampling method reduces the number of points without loss of relevant scene information. It is experimentally compared with other systematic approaches for the case of actual scene genetic registration.