Segregator: Global Point Cloud Registration with Semantic and Geometric Cues (2301.07425v2)

Abstract: This paper presents Segregator, a global point cloud registration framework that exploits both semantic information and geometric distribution to efficiently build up outlier-robust correspondences and search for inliers. Current state-of-the-art algorithms rely on point features to set up putative correspondences and refine them by employing pair-wise distance consistency checks. However, such a scheme suffers from degenerate cases, where the descriptive capability of local point features downgrades, and unconstrained cases, where length-preserving (l-TRIMs)-based checks cannot sufficiently constrain whether the current observation is consistent with others, resulting in a complexified NP-complete problem to solve. To tackle these problems, on the one hand, we propose a novel degeneracy-robust and efficient corresponding procedure consisting of both instance-level semantic clusters and geometric-level point features. On the other hand, Gaussian distribution-based translation and rotation invariant measurements (G-TRIMs) are proposed to conduct the consistency check and further constrain the problem size. We validated our proposed algorithm on extensive real-world data-based experiments. The code is available: https://github.com/Pamphlett/Segregator.

• The paper introduces Segregator, a framework that leverages semantic and geometric cues to enable robust, outlier-free point cloud registration.
• The paper formulates a dual approach that couples semantic instance-level clusters with geometric point features for reliable correspondence establishment.
• The paper presents G-TRIM, a Gaussian-based consistency check that improves registration accuracy and computational efficiency compared to traditional methods.

Overview of "Segregator: Global Point Cloud Registration with Semantic and Geometric Cues"

The paper presents "Segregator," a novel framework for global point cloud registration, which leverages both semantic and geometric cues to achieve robust outlier-free correspondence and inlier detection. This research addresses critical challenges in the current state-of-the-art methodologies, particularly those relying on point features for correspondence setup and refinement. These methods commonly falter in degenerate and unconstrained cases, leading to computational complexity and reduced efficacy in environments such as simultaneous localization and mapping (SLAM) tasks in mobile robotics.

Key Contributions

1. Semantic-Enhanced Registration Framework: The Segregator framework introduces the novel use of semantic information alongside geometric distributions to create a robust point cloud registration system. It efficiently constructs correspondences while being resilient against outliers—crucial in autonomous driving scenarios where low overlap in LiDAR scans is prevalent.
2. Degeneracy-Robust Correspondence Establishment: The paper proposes a mechanism that couples semantic instance-level clusters with geometric point features to form reliable correspondences. This dual approach addresses the descriptive weaknesses of local point features in expansive areas and guarantees comprehensive identification of potential inliers, thereby reducing the problem size.
3. Gaussian Distribution-Based Consistency Check (G-TRIM): The introduction of Gaussian distribution-based translation and rotation invariant measurements (G-TRIMs) significantly enhances the consistency check mechanism, surpassing traditional length-preserving methods. This novel approach not only evaluates positional similarities but also incorporates structural distributions, hence improving accuracy and computational efficiency.

Experimental Validation

The researchers validated the proposed algorithm using extensive real-world datasets, particularly from the KITTI database. Comparative analyses with existing methods, including TEASER++, Quatro, and V-GICP, demonstrated Segregator's superiority in handling various perturbations and maintaining high success rates across different testing scenarios. Notably, Segregator's performance remained relatively consistent even with substantial semantic label deterioration, indicating robustness in less-than-ideal semantic mapping conditions.

Implications and Future Directions

The introduction of Segregator holds significant implications for the field of robotics and computer vision, offering an enhanced approach to point cloud registration. Practically, this framework can improve the robustness and efficiency of SLAM systems, which are critical for autonomous navigation and robotic perception.

Theoretically, the use of semantic information in conjunction with Gaussian distribution metrics provides a compelling avenue for advancing global registration methodologies. Future work could explore the integration of Segregator with probabilistic modeling techniques to further refine pose estimation accuracy and resilience against outliers. Additionally, extending this framework to support real-time processing across varied environmental conditions could broaden its applicability in dynamic and complex scenarios.

Overall, the Segregator framework represents an important step forward in the domain of outlier-robust point cloud registration, offering both practical enhancements and new theoretical insights into correspondence establishment and consistency checking.