Pyramid Semantic Graph-based Global Point Cloud Registration with Low Overlap (2307.12116v1)

Abstract: Global point cloud registration is essential in many robotics tasks like loop closing and relocalization. Unfortunately, the registration often suffers from the low overlap between point clouds, a frequent occurrence in practical applications due to occlusion and viewpoint change. In this paper, we propose a graph-theoretic framework to address the problem of global point cloud registration with low overlap. To this end, we construct a consistency graph to facilitate robust data association and employ graduated non-convexity (GNC) for reliable pose estimation, following the state-of-the-art (SoTA) methods. Unlike previous approaches, we use semantic cues to scale down the dense point clouds, thus reducing the problem size. Moreover, we address the ambiguity arising from the consistency threshold by constructing a pyramid graph with multi-level consistency thresholds. Then we propose a cascaded gradient ascend method to solve the resulting densest clique problem and obtain multiple pose candidates for every consistency threshold. Finally, fast geometric verification is employed to select the optimal estimation from multiple pose candidates. Our experiments, conducted on a self-collected indoor dataset and the public KITTI dataset, demonstrate that our method achieves the highest success rate despite the low overlap of point clouds and low semantic quality. We have open-sourced our code https://github.com/HKUST-Aerial-Robotics/Pagor for this project.

Insights into Pyramid Semantic Graph-based Global Point Cloud Registration with Low Overlap

The paper "Pyramid Semantic Graph-based Global Point Cloud Registration with Low Overlap" addresses the significant challenge of achieving robust global point cloud registration under conditions of low overlap, a common situation in practical robotics tasks due to factors such as occlusion and viewpoint transformations. This challenge is pivotal for applications like loop closing, relocalization, and object pose estimation, which are fundamental to the effective operation of mobile robots. The authors propose a novel solution leveraging a graph-theoretic framework integrated with semantic and geometric cues, delivering enhanced robustness and accuracy.

Technical Approach and Novel Contributions

The authors introduce a multi-layered consistency graph approach, which they term the "pyramid semantic graph." This paradigm shifts from a single consistency threshold to multiple levels, counteracting the limitations posed by unpredictable noise distributions across different scenarios. The authors realize that semantic cues can efficiently scale down the problem size by reducing the density of point clouds, which also complements the development of a more stable data association due to the reduced dimensionality and enhanced semantic landmarks.

Key contributions of this paper include:

• Pyramid Semantic Graph Construction: The proposed method builds a pyramid graph with varying consistency thresholds to robustly handle ambiguity and improve the estimation of transformations. This is done by constructing semantic landmarks at the front-end, making sure to preserve high-fidelity features that contribute to a strong correspondence base while maintaining manageable data sizes.
• Cascaded Gradient Ascend Method: To solve the densest clique problem, the authors devise a cascaded gradient ascend approach. This offers an optimization-based iterative method that efficiently iterates over the pyramid graph’s multiple levels, using previously computed results as initial conditions for subsequent computations.
• Integration of Geometric Verification: The paper uniquely integrates semantic and geometric data, employing a geometric verification process that ascertains the validity of transformation candidates. This "distrust and verify" strategy ensures a robust selection of the optimal transformation against dense metric points, enhancing the reliability of the registration results.

Experimental Validation and Results

The authors validate their methodology using challenging datasets collected in both indoor (e.g., university building) and outdoor (e.g., KITTI) environments. The experimental outcomes reveal a superior success rate in global registration tasks despite low overlap and semantic irregularity, surpassing the performance of state-of-the-art methods such as TEASER++, Go-ICP, and RANSAC. This achievement highlights the effectiveness of employing multiple consistency thresholds and integrating semantic and geometric cues to address the inherent ambiguity in low-overlap conditions.

Implications and Future Directions

The proposed pyramid semantic graph method paves the way for more resilient point cloud registration frameworks, encouraging the development of robust systems capable of operating in diverse and challenging environments. Practical implications of this method can extend to various fields such as autonomous navigation, scene understanding, and multi-session mapping, where accurate pose estimation and data association are critical.

Future research could explore the extension of this framework to even more complex environments, with considerations for real-time applications and scalability. Furthermore, advancements might include deeper integrations of machine learning techniques to dynamically adjust consistency thresholds based on learned environmental cues, potentially automating and optimizing the registration process across broader applications.

The paper provides a substantial foundation for evolving the intersection of graph theory and semantic feature analysis within the domain of robotics-focused computer vision, presenting a compelling direction for future exploration in AI-driven perception and cognition for autonomous systems.