Future autonomous satellite repair missions would benefit from higher accuracy pose estimates of target satellites. Constraint analysis provides a sensitivity index which can be used as a registration accuracy predictor. It was shown that point cloud configurations with higher values of this index returned more accurate pose estimates than unstable configurations with lower index values. Registration tests were conducted on four satellite geometries using synthetic range data. These results elucidate a means of determining the optimal scanning area of a given satellite for registration with the Iterative Closest Point (ICP) algorithm to return a highly accurate pose estimate.