  The objective of this work is to propose a new algorithm to fit a sphere on a
noisy 3D point cloud distributed around a complete or a truncated sphere. More
precisely, we introduce a projected Robbins-Monro algorithm and its averaged
version for estimating the center and the radius of the sphere. We give
asymptotic results such as the almost sure convergence of these algorithms as
well as the asymptotic normality of the averaged algorithm. Furthermore, some
non-asymptotic results will be given, such as the rates of convergence in
quadratic mean. Some numerical experiments show the efficiency of the proposed
algorithm on simulated data for small to moderate sample sizes.
