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In this paper, an algorithm is presented for automatic registration of terrestrial point clouds based on reflectivity images captured from terrestrial laser scanner. Firstly, the Moravec interest operator is used to extract feature points in the left one of two adjacent images and probabilistic relaxation is employed to match corresponding points for those feature points. The strategy of matching on image pyramid is used to improve the reliability and speed of image matching. Reflectivity images usually have low resolution, moreover, distinct geometric difference exits between adjacent images which are close-ranged. Consequently, the probability of erroneous matching becomes high. Therefore, geometric constraint (i.e. distance invariance) of 3D corresponding point pairs is used to eliminate erroneous corresponding point pairs. Iterative matching process is implemented to acquire high accuracy and stability. Thereafter, absolute orientation in photogrammetry is employed to compute six transformation parameters separated in rotation and translation. Experiments were implemented to testify the method, presented in this paper, on indoor and outdoor point clouds. Processes for those point clouds are fully automatic and acquire a good accuracy up to the order of millimeter.