Spectral Geometric Verification: Re-Ranking Point Cloud Retrieval for Metric Localization
Kavisha Vidanapathirana, Peyman Moghadam, Sridha Sridharan, Clinton, Fookes

TL;DR
This paper introduces SpectralGV, an efficient spectral method for geometric verification in large-scale metric localization that improves candidate re-ranking without requiring resource-intensive registration.
Contribution
SpectralGV is a novel, registration-free spectral approach for geometric verification that enhances re-ranking in point cloud retrieval for metric localization.
Findings
SpectralGV outperforms existing re-ranking methods in large-scale datasets.
It improves recall and pose estimation accuracy.
The method has negligible impact on runtime.
Abstract
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…
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Taxonomy
TopicsRobotics and Sensor-Based Localization · Human Pose and Action Recognition · 3D Shape Modeling and Analysis
