Spectral Geometric Verification: Re-Ranking Point Cloud Retrieval for Metric Localization (2210.04432v2)

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 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.

• The paper introduces SpectralGV, a spectral technique that re-ranks point cloud retrievals to improve 6DoF localization accuracy without intensive registration.
• It leverages a spatial consistency graph and parallel computation to robustly handle noisy correspondences in point cloud data.
• Experimental results on diverse large-scale datasets show significant gains in Recall@1 and MRR with minimal runtime impact.

Overview of Spectral Geometric Verification for Point Cloud Retrieval

In the field of large-scale metric localization, precise and efficient retrieval of correct location estimates from point cloud data is of paramount importance. The paper introduces a novel approach, termed Spectral Geometric Verification (SpectralGV), aimed at enhancing retrieval processes within the framework of metric localization by re-ranking point cloud retrievals to ensure higher accuracy in 6 Degrees of Freedom (6DoF) pose estimations.

Core Contributions

The central contribution of the paper is the development of SpectralGV, an efficient spectral method for geometric verification that omits the need for computationally intensive registration processes. By leveraging spectral techniques, the method evaluates spatial consistency between point clouds and re-ranks retrieval candidates based on a score that quantifies geometric compatibility without direct registration.

Key highlights of SpectralGV include:

• Deterministic and Parallel Computation: Unlike conventional methods that require sequential processing, SpectralGV allows for parallel computation across all candidates, significantly enhancing computational efficiency.
• Robustness to Outliers: SpectralGV exhibits resilience to noisy correspondences, thereby ensuring reliable performance across diverse dataset conditions.
• Integration with State-of-the-Art Architectures: The method augments existing architectures without altering their structural design, providing an agile solution for performance enhancement.

Methodology

The methodology underpinning SpectralGV involves constructing a correspondence compatibility graph between query point clouds and each candidate in the retrieval set. An inter-cluster score derived from this graph assesses the spatial correspondence reliability. By identifying clusters of spatially consistent correspondences, SpectralGV effectively evaluates potential matches retrieved during the initial retrieval process. The principal eigenvector of the graph's adjacency matrix is harnessed to compute a robust fitness score, circumventing direct registration.

Experimental Validation

The experimental validation is comprehensive, utilizing five large-scale datasets, including both "easy" and "hard" test cases, such as MulRan DCC and KITTI-360. SpectralGV consistently outperforms other re-ranking methods and baseline results, demonstrating pronounced improvements in Recall@1 and Mean Reciprocal Rank (MRR) metrics. Moreover, the method shows negligible runtime impact even when scaling the re-ranking process, a critical advantage for real-time applications.

Implications and Future Directions

The implications of SpectralGV are multifaceted:

• Enhanced Localization Accuracy: By refining retrieval candidate rankings, the methodology facilitates more accurate localization in complex environments characterized by structural similarity.
• Scalability and Real-Time Application: The method's efficiency ensures applicability to city-scale scenarios and beyond, where speed and accuracy are pivotal.
• Compatibility Across Architectures: Its agnostic nature towards underlying architectures ensures broad applicability across different metric localization frameworks.

Future research could explore the integration of SpectralGV with newer architectures that encompass advancements in point cloud processing and retrieval. Moreover, exploring the potential of SpectralGV in cross-modal scenarios, where point clouds are used alongside visual data, could open new avenues for enhancing multi-sensor localization systems. Investigating the use of SpectralGV in decentralized and collaborative robotic systems where distributed processing and rapid decision-making are crucial may also present an exciting perspective.

In conclusion, SpectralGV offers a compelling advancement in point cloud retrieval for metric localization, ensuring both enhanced performance and computational efficiency, making it a pivotal tool for autonomous systems requiring precise spatial understanding in large-scale environments.