Global registration of point clouds for mapping

Abstract

We present a robust Global Registration technique focused on environment survey 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. Candidate 3D matches are estimated by aligning the 2D projective representations of the acquired scans, and benefiting from the corresponding dimensional reduction. Each single candidate match is then validated exploiting the implicit empty space information associated to scans. The global reconstruction problem is modeled as a directed graph, where scan poses (nodes) are connected through matches (edges). This is exploited to compute local matches (instead of global ones) between pairs of scans that are in the same reference frame. As a consequence, both performance and recall ratio increase w.r.t. using only global matches. Additionally, 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 datasets.