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For both indoor and outdoor environments, we propose an efficient and novelmethod for different scales and sparse 3D point clouds registration that cannotbe handled by the current popular ICP approaches. Our algorithm efficientlydetects the scale difference between point clouds and uses the keyframes toestimate the relative pose for calculating the scale difference. The algorithmapplies a filter and computes the final transformation which coverages to aglobal minimum. The good estimation of transform and scale helps in thecalculation of the covariance matrix using a closed form solution efficiently.This covariance between point clouds helps in the estimation of informationmatrix for pose-graph SLAM.

PCR-Pro: 3D Sparse and Different Scale Point Clouds Registration and Robust Estimation of Information Matrix For Pose Graph SLAM