We proposed a global positioning technique in 3D environment using 3D geometrical map and a RGB-D camera based on a ND (Normal Distributions) voxel matching. Firstly, a 3D geometrical map represented by point-cloud is converted to ND voxels, and eigen ellipses are extracted. Meanwhile, ND voxels are also created from a range image captured by a RGB-D camera, and eigen ellipses and seven representative points are calculated in each ND voxel. For global localization, point-plane and plane-plane correspondences are tested and an optimum global position is determined using a particle filter. Experimental results show that the proposed technique is robust for the similarity in a 3D map and converges more stably than a standard maximum likelihood method using a beam model.