DCPCR: Deep Compressed Point Cloud Registration in Large-Scale Outdoor Environments

Abstract

Reliable and accurate registration of point clouds is a challenging problem in robotics as well as in the domain of autonomous driving. In this article, we address the task of aligning point clouds with low overlap, containing moving objects, and without prior information about the initial guess. We enhance classical ICP-based registration with neural feature-based matching to reliably find point correspondences. Our novel 3D convolutional and attention-based network is trained in an end-to-end fashion to learn features, which are well suited for matching and for rating the quality of the point correspondences. By utilizing a compression encoder, we can directly operate on a compressed map representation, making our approach well suited for operation under memory constraints. We evaluate our approach on point clouds obtained at completely different points in time, showing that our approach is able to register point clouds even under those challenging conditions reliably.