A Single Correspondence Is Enough: Robust Global Registration to Avoid Degeneracy in Urban Environments (2203.06612v1)

Abstract: Global registration using 3D point clouds is a crucial technology for mobile platforms to achieve localization or manage loop-closing situations. In recent years, numerous researchers have proposed global registration methods to address a large number of outlier correspondences. Unfortunately, the degeneracy problem, which represents the phenomenon in which the number of estimated inliers becomes lower than three, is still potentially inevitable. To tackle the problem, a degeneracy-robust decoupling-based global registration method is proposed, called Quatro. In particular, our method employs quasi-SO(3) estimation by leveraging the Atlanta world assumption in urban environments to avoid degeneracy in rotation estimation. Thus, the minimum degree of freedom (DoF) of our method is reduced from three to one. As verified in indoor and outdoor 3D LiDAR datasets, our proposed method yields robust global registration performance compared with other global registration methods, even for distant point cloud pairs. Furthermore, the experimental results confirm the applicability of our method as a coarse alignment. Our code is available: https://github.com/url-kaist/quatro.

• The paper introduces Quatro, a global registration method addressing urban degeneracy by using environmental properties and needing only a single correspondence for transformation.
• The method employs a novel quasi-SO(3) rotation estimation and decouples rotation from translation to enable robust registration even with minimal inliers.
• Experiments show Quatro outperforms benchmarks in challenging scenarios, demonstrating robust performance with sparse correspondences and minimal input data.

Overview of "A Single Correspondence Is Enough: Robust Global Registration to Avoid Degeneracy in Urban Environments"

The paper presents a global registration method, termed Quatro, to address the degeneracy challenge in urban environments. This degeneracy arises during registration when the estimated inliers are insufficient—typically fewer than three in 3D spaces—often leading to failure in computing a global transformation between the source and target point clouds. The authors propose leveraging the Atlanta world assumption, which simplifies 3D rotation estimation to a quasi-SO(3) problem, reducing the degrees of freedom (DoF) from the standard three to one.

Key Contributions

1. Quasi-SO(3) Estimation
   • The authors introduce a novel rotation estimation methodology that assumes urban environments are conducive to approximating the rotation primarily through the yaw component. By leveraging property of urban settings—predominantly flat with structures orthogonal to the ground plane—the method dramatically reduces the necessity for as many inlier correspondences.
2. Addressing Degeneracy
   • Quatro's reduction of the minimum required correspondences to a single pair is pivotal. This technique effectively manages scenarios where degeneracy might typically undermine registration, thus enabling robust estimation even with minimal reliable correspondences.
3. Decoupling Rotation and Translation
   • The proposed algorithm decouples the estimation of rotation from translation, leveraging quasi-SO(3) estimation followed by component-wise translation estimation (COTE). This decoupling facilitates managing the degeneracy issue effectively by identifying and utilizing stable rotational components.
4. Implementation and Performance
   • Experimental results on indoor and outdoor datasets, including KITTI, demonstrate that Quatro maintains commendable registration performance even as the challenges of distant or partially overlapping point clouds increase. Its robustness is marked by the ability to function effectively with less input data than traditional methods.

Experimental Insights

The paper showcases the efficacy of Quatro through numerous quantitative and qualitative analyses undertaken on standard datasets such as KITTI and NAVER LABS localization dataset. It demonstrates a significant performance gain over benchmarks like RANSAC and TEASER++, particularly in scenarios characterized by severe correspondence sparsity. The performance is evaluated based on the average relative pose errors and success rates, and Quatro exhibits more consistent results across challenging conditions.

Implications and Future Work

The proposed method, Quatro, is a stride forward in robust global registration, particularly in urban environments where previous methods struggle with degeneracy. Practical implications include enhanced mapping and navigation systems, particularly for autonomous vehicles operating in complex urban settings. Furthermore, the algorithm might be adaptable to other perception platforms like UAVs, where orientation estimation in dynamic and irregular terrains is necessary.

Theoretically, Quatro opens up new frontiers in reducing dimensionality in correspondence-based methods, potentially benefiting areas such as SLAM and 3D reconstruction. Future work could delve into integrating improvements like leveraging INS data for non-flat regions, extending its robustness across various geographic topographies. Additionally, exploring machine learning-based enhancements could further optimize feature matching and correspondence selection for global registration tasks.

In summary, the work presents a notable contribution to the field of computer vision and robotics, tackling a critical problem with innovative rotation estimation, thereby extending the operational robustness and real-time efficacy of mobile platforms engaged in urban navigation. The findings have potential implications not only for current mapping solutions but also for broader applications in computer vision and spatial analytics.