The registration problem of multiple range images is fundamental for many applications that rely on precise geometric models. We propose a robust registration method that can align multiple range images comprised of a large number of data points. The proposed method minimizes an error function that is constructed to be global against all range images, providing the ability to diffusively distribute errors instead of accumulating them. The minimization strategy is designed to be efficient and robust against outliers by using conjugate gradient search utilizing M-estimator. Also, for "better" point correspondence search, the laser reflectance strength is used as an additional attribute of each 3D data point. For robustness against data noise, the framework is designed not to use secondary information, i.e. surface normals, in its error metric. We describe the details of the proposed method, and present experimental results applying the proposed method to real data. © 2008 Springer-Verlag US.