G3Reg: Pyramid Graph-based Global Registration using Gaussian Ellipsoid Model

Abstract: This study introduces a novel framework, G3Reg, for fast and robust global registration of LiDAR point clouds. In contrast to conventional complex keypoints and descriptors, we extract fundamental geometric primitives, including planes, clusters, and lines (PCL) from the raw point cloud to obtain low-level semantic segments. Each segment is represented as a unified Gaussian Ellipsoid Model (GEM), using a probability ellipsoid to ensure the ground truth centers are encompassed with a certain degree of probability. Utilizing these GEMs, we present a distrust-and-verify scheme based on a Pyramid Compatibility Graph for Global Registration (PAGOR). Specifically, we establish an upper bound, which can be traversed based on the confidence level for compatibility testing to construct the pyramid graph. Then, we solve multiple maximum cliques (MAC) for each level of the pyramid graph, thus generating the corresponding transformation candidates. In the verification phase, we adopt a precise and efficient metric for point cloud alignment quality, founded on geometric primitives, to identify the optimal candidate. The algorithm's performance is validated on three publicly available datasets and a self-collected multi-session dataset. Parameter settings remained unchanged during the experiment evaluations. The results exhibit superior robustness and real-time performance of the G3Reg framework compared to state-of-the-art methods. Furthermore, we demonstrate the potential for integrating individual GEM and PAGOR components into other registration frameworks to enhance their efficacy. Code: https://github.com/HKUST-Aerial-Robotics/G3Reg

• The paper introduces a novel global registration framework using a Gaussian Ellipsoid Model to robustly align LiDAR point clouds.
• It leverages a pyramid compatibility graph with a distrust-and-verify scheme to efficiently solve the maximum cliques problem for data association.
• The approach demonstrates substantial improvements in registration accuracy and efficiency over state-of-the-art methods across diverse datasets.

Overview of "G3Reg: Pyramid Graph-based Global Registration using Gaussian Ellipsoid Model"

The paper introduces G3Reg, a framework for the global registration of LiDAR point clouds. This framework addresses the limitations associated with traditional keypoint and descriptor-based methods by utilizing a novel approach that involves the extraction of geometric primitives (planes, clusters, and lines) to develop a Gaussian Ellipsoid Model (GEM) representation. This model ensures a high probability that ground truth centers are encompassed, allowing for a more robust and efficient registration process.

Methodology

G3Reg comprises two key innovations: the GEM-based data association and a distrust-and-verify scheme facilitated by a Pyramid Compatibility Graph for Global Registration (PAGOR). The framework is built on three principal stages:

1. GEM Extraction: Geometric primitives are extracted from raw point clouds, and each is parameterized by the proposed GEM. This encapsulates both a statistical representation of segments and a pseudo-Gaussian parameter to account for uncertainties in segment centers.
2. Pyramid Compatibility Graph: The second innovation involves constructing a compatibility graph to test the pairwise compatibility of GEMs using a multi-threshold compatibility test. This graph-theoretical approach efficiently solves the maximum cliques problem to identify potential inlier sets.
3. Transformation Estimation and Verification: Multiple candidate transformations are generated from MACs, and an evaluation function based on the Chamfer distance is employed to verify and select the most suitable transformation. The function accounts for the geometric fidelity of the alignment to maximize registration quality.

Numerical Results and Contributions

G3Reg was validated against several datasets, including public and self-collected multi-session datasets, demonstrating superior performance in registration accuracy and time efficiency compared to state-of-the-art approaches like FPFH and TEASER++. Notably, it achieves a high recall rate across diverse environmental scenarios, with improvements in handling challenging cases characterized by low overlaps and large viewpoint differences.

The framework's contributions are multi-faceted:

• Robustness and Efficiency: The use of GEMs mitigates issues evident in descriptor consistency from traditional models, providing an efficient registration process even in the presence of high-density variations and occlusions.
• Generalization to Various Scenarios: By maintaining consistent parameter settings across different scenarios, including various LiDAR types, the approach demonstrates a high degree of generalization.
• Enhancement of Existing Frameworks: The authors advocate the integration of individual components, like GEM and PAGOR, into other frameworks to enhance their effectiveness.

Implications and Future Directions

The implications of G3Reg are pivotal, paving the way for more reliable and real-time solutions in robotics and autonomous systems, particularly in tasks such as loop closure and multi-session simultaneous localization and mapping (SLAM). The proposed distrust-and-verify paradigm provides significant flexibility in handling uncertainty and optimizing transformation selection.

For future work, the enhancement of segment-based methodologies through higher-level descriptors or incorporating semantic information could be explored. Additionally, advanced techniques for detecting the localizability of environments could further augment the robustness of point cloud registration methods in complex and unstructured environments.

G3Reg stands as a significant advancement in the field of global registration of LiDAR point clouds, contributing to more resilient and adaptable solutions necessary for real-world applications.