We consider the problem of jointly registering multiple point sets using rigid transforms. We propose a distributed algorithm based on consensus optimization for the least-squares formulation of this problem. In each iteration, the computation is distributed among the point sets and the results are averaged. For each point set, the dominant cost per iteration is the SVD of a square matrix of size d, where d is the ambient dimension. Existing methods for joint registration are either centralized or perform the optimization sequentially. The proposed algorithm is naturally more scalable than these methods. As an application, we integrate the proposed algorithm within a divide-and-conquer approach for sensor network localization. In particular, we are able to localize very large networks, which are beyond the scope of most existing localization methods.