  For both indoor and outdoor environments, we propose an efficient and novel
method for different scales and sparse 3D point clouds registration that cannot
be handled by the current popular ICP approaches. Our algorithm efficiently
detects the scale difference between point clouds and uses the keyframes to
estimate the relative pose for calculating the scale difference. The algorithm
applies a filter and computes the final transformation which coverages to a
global minimum. The good estimation of transform and scale helps in the
calculation of the covariance matrix using a closed form solution efficiently.
This covariance between point clouds helps in the estimation of information
matrix for pose-graph SLAM.
