Global matching of point clouds for scan registration and loop detection

Abstract

We present a robust Global Matching technique focused on 3D mapping applications using laser range-finders. Our approach works under the assumption that places can be recognized by analyzing the projection of the observed points along the gravity direction. Relative poses between pairs of 3D point clouds are estimated by aligning their 2D projective representations and benefiting from the corresponding dimensional reduction. We present the complete processing pipeline for two different applications that use the global matcher as a core component: First, the global matcher is used for the registration of static scan sets where no a-priori information of the relative poses is available. It is combined with an effective procedure for validating the matches that exploits the implicit empty space information associated to single acquisitions. In the second use case, the global matcher is used for the loop detection required for 3D SLAM applications. We use an Extended Kalman Filter to obtain a belief of the map poses, which allows to validate matches and to execute hierarchical overlap tests, which reduce the number of potential matches to be evaluated. Additionally, the global matcher is combined with a fast local technique. In both use cases, the global reconstruction problem is modeled as a sparse graph, where scan poses (nodes) are connected through matches (edges). The graph structure allows formulating a sparse global optimization problem that optimizes scan poses, considering simultaneously all accepted matches. Our approach is being used in production systems and has been successfully evaluated on several real and publicly available datasets.