L-PR: Exploiting LiDAR Fiducial Marker for Unordered Low Overlap Multiview Point Cloud Registration (2406.03298v2)

Abstract: Point cloud registration is a prerequisite for many applications in computer vision and robotics. Most existing methods focus on pairwise registration of two point clouds with high overlap. Although there have been some methods for low overlap cases, they struggle in degraded scenarios. This paper introduces a novel framework dubbed L-PR, designed to register unordered low overlap multiview point clouds leveraging LiDAR fiducial markers. We refer to them as LiDAR fiducial markers, but they are the same as the popular AprilTag and ArUco markers, thin sheets of paper that do not affect the 3D geometry of the environment. We first propose an improved adaptive threshold marker detection method to provide robust detection results when the viewpoints among point clouds change dramatically. Then, we formulate the unordered multiview point cloud registration problem as a maximum a-posteriori (MAP) problem and develop a framework consisting of two levels of graphs to address it. The first-level graph, constructed as a weighted graph, is designed to efficiently and optimally infer initial values of scan poses from the unordered set. The second-level graph is constructed as a factor graph. By globally optimizing the variables on the graph, including scan poses, marker poses, and marker corner positions, we tackle the MAP problem. We conduct both qualitative and quantitative experiments to demonstrate that the proposed method surpasses previous state-of-the-art (SOTA) methods and to showcase that L-PR can serve as a low-cost and efficient tool for 3D asset collection and training data collection. In particular, we collect a new dataset named Livox-3DMatch using L-PR and incorporate it into the training of the SOTA learning-based method, SGHR, which brings evident improvements for SGHR on various benchmarks.

• The paper introduces L-PR, which improves registration accuracy by integrating adaptive LiDAR marker detection with MAP estimation.
• It employs a two-level graph approach to compute precise pose estimates and achieve high-fidelity point cloud reconstructions.
• Experimental results demonstrate L-PR’s superior performance in low-overlap and degraded scenes, setting a new benchmark in the field.

Analysis of L-PR: Leveraging LiDAR Fiducial Markers for Multiview Point Cloud Registration

The paper "L-PR: Exploiting LiDAR Fiducial Marker for Unordered Low Overlap Multiview Point Cloud Registration" presents a novel framework aimed at addressing the challenges associated with low overlap point cloud registration in multiview settings. This paper is situated in the broader context of computer vision and robotics, where accurate point cloud registration is essential for a variety of applications, including 3D reconstruction and localization.

Methodological Advancements

The authors introduce a framework named L-PR, which utilizes LiDAR fiducial markers—referred to interchangeably as AprilTag and ArUco markers—to enhance the registration of low overlap multiview point clouds. A notable contribution is the adaptive threshold marker detection method, which increases robustness against significant viewpoint changes, a common challenge in real-world applications. Additionally, the registration problem is formulated as a maximum a-posteriori (MAP) estimation, solved through a two-level graph process. The first-level weighted graph efficiently computes initial pose estimates, while the second-level graph, structured as a factor graph, globally optimizes these estimates.

Experimental Evaluation and Results

The empirical analysis focuses on diverse scenarios to validate the framework's effectiveness. Quantitative comparisons against state-of-the-art methods like SGHR and Teaser++ demonstrate superior performance across several metrics:

• Registration Accuracy: The proposed method consistently provides low RMSE values in both rotational and translational error across all test scenes, highlighting its precision relative to competing techniques.
• Instance Reconstruction Quality: Evaluations using the Chamfer Distance and recall metrics further underscore the method's ability to produce high-fidelity reconstructions, showcasing minimal surface noise and drift.
• Robustness in Degraded Scenes: The proposed method excels in scenarios characterized by repetitive structures or low textures, where it outperforms existing methods.

Furthermore, the paper details the implementation, openly sharing code and datasets, which underscores a commitment to transparency and reproducibility in research.

Implications and Future Directions

The introduction of L-PR has significant implications for both practical applications and theoretical advancements. Practically, the framework extends the utility of LiDAR systems in challenging environments, potentially impacting sectors such as autonomous navigation and AR. Theoretically, the work opens avenues for further research into robust marker detection methods and efficient optimization techniques in unordered settings.

Future research directions might explore the integration of L-PR with machine learning approaches to enhance its adaptability to novel and dynamic environments, thereby addressing current limitations in marker deployment. Additionally, investigations into leveraging synthetic data for training and testing could further refine marker-based registration techniques.

In summary, this paper contributes a solid and practical framework for multiview point cloud registration using LiDAR fiducial markers, offering compelling results backed by rigorous experimentation. The open-sourcing of tools and datasets further amplifies the impact, setting a valuable precedent for future studies in this domain.