In large-scale metric localization, an incorrect result during retrieval will lead to an incorrect pose estimate or loop closure. Re-ranking methods propose to take into account all the top retrieval candidates and re-order them to increase the likelihood of the top candidate being correct. However, state-of-the-art re-ranking methods are inefficient when re-ranking many potential candidates due to their need for resource intensive point cloud registration between the query and each candidate. In this work, we propose an efficient spectral method for geometric verification (named SpectralGV) that does not require registration. We demonstrate how the optimal inter-cluster score of the correspondence compatibility graph of two point clouds represents a robust fitness score measuring their spatial consistency. This score takes into account the subtle geometric differences between structurally similar point clouds and therefore can be used to identify the correct candidate among potential matches retrieved by global similarity search. SpectralGV is deterministic, robust to outlier correspondences, and can be computed in parallel for all potential candidates. We conduct extensive experiments on 5 large-scale datasets to demonstrate that SpectralGV outperforms other state-of-the-art re-ranking methods and show that it consistently improves the recall and pose estimation of 3 state-of-the-art metric localization architectures while having a negligible effect on their runtime. The open-source implementation and trained models are available at: https://github.com/csiro-robotics/SpectralGV.
翻译:在大规模衡量本地化中,检索过程中的结果不正确,将导致不正确的表面估计或环状关闭。 重新排序方法建议考虑所有顶级检索候选人,并重新排序,以增加高级候选人正确的可能性。 但是,在重新排列许多潜在候选人时,最先进的重新排序方法效率低下,原因是他们需要在查询人和每个候选人之间进行资源密集点云登记。 在这项工作中,我们提出一个不需要注册的测量核查的高效光谱方法( 名为 SpectralGV ) 。 我们展示了两点云通信兼容性图的最佳分组间评分代表了衡量其空间一致性的稳健健的健身得分。 这个评分考虑到了结构上相似的云之间的细微几度差异,因此可以用来确定全球类似搜索所检索的潜在匹配的正确人选。 SpectralGV 具有确定性, 强于超度通信, 并且可以对所有潜在候选人进行平行计算。 我们在5个大型数据集上进行广泛的实验, 以显示SpectralGV 匹配率超过其空间一致性得分, 也持续地显示其基础/ 基础结构的升级法的不断改进。</s>