Robust Cross-Modality Point Cloud Registration with Feature Filtering and Local-Global Optimization

แนวคิดหลัก

A robust cross-modality point cloud registration framework that combines feature filtering and local-global optimization to achieve state-of-the-art performance.

บทคัดย่อ

The paper proposes a cross-modality point cloud registration framework called FF-LOGO that addresses the challenges in aligning point clouds from different sensor modalities. The key components of the framework are:

1. Cross-Modality Feature Correlation Filtering Module:

   • Extracts geometric transformation-invariant features from cross-modality point clouds using a Geometric Self-Attention mechanism.
   • Performs feature matching and point selection to obtain an initial pose estimation.

2. Local Adaptive Key Region Aggregation Module:

   • Identifies dispersed and geometrically representative key points in the point cloud using Farthest Point Sampling.
   • Aggregates neighboring points around the key points to form local adaptive key regions.

3. Global Modality Consistency Fusion Optimization Module:

   • Matches the local adaptive key regions with the cross-modality feature-coupled point set to compute point-to-plane residuals.
   • Performs local-to-global optimization to refine the initial pose estimation and obtain the final optimized transformation.

The proposed method significantly outperforms the current state-of-the-art on the 3DCSR dataset, improving the recall rate from 40.59% to 75.74%. The authors also demonstrate the practical application of FF-LOGO for cross-modality localization on a bipedal wheeled robot.

สถิติ

The 3DCSR dataset contains point clouds from three different modalities: LiDAR, Kinect, and camera sensors.
LiDAR point clouds are relatively sparse, while Kinect point clouds are dense and uniform.
The dataset provides ground truth transformations for aligning either LiDAR or SfM geometry with dense Kinect geometry.

คำพูด

"Our method fully leverages the advantages of deep learning in fuzzy correspondence and traditional optimization in pose fine-tuning for cross-modality registration and achieve the state-of-the-art with an improvement from 40.59% to 75.74%."

ข้อมูลเชิงลึกที่สำคัญจาก

FF-LOGO: Cross-Modality Point Cloud Registration with Feature Filtering and Local to Global Optimization

by Nan Ma,Mohan... ที่ arxiv.org 04-15-2024

https://arxiv.org/pdf/2309.08966.pdf

สอบถามเพิ่มเติม

How can the proposed framework be extended to handle dynamic scenes or non-rigid transformations in cross-modality point cloud registration

To extend the proposed framework to handle dynamic scenes or non-rigid transformations in cross-modality point cloud registration, several modifications and additions can be implemented. Firstly, incorporating dynamic feature extraction techniques that can adapt to changing environments and non-rigid transformations would be essential. This could involve integrating methods like dynamic graph CNNs or recurrent neural networks to capture temporal information and deformations in the point clouds. Additionally, introducing a mechanism for temporal coherence and motion estimation could help in tracking and registering dynamic scenes effectively. Utilizing spatio-temporal feature correlation and motion prediction models can enhance the framework's ability to handle dynamic scenarios. Furthermore, incorporating probabilistic models or Bayesian approaches to account for uncertainties in non-rigid transformations and dynamic scenes would improve the robustness and accuracy of the registration process.

What are the potential limitations of the local-global optimization approach, and how could it be further improved to handle more challenging scenarios

While the local-global optimization approach in FF-LOGO offers significant improvements in registration accuracy, there are potential limitations that need to be addressed for handling more challenging scenarios. One limitation is the scalability of the optimization process, especially when dealing with large-scale point clouds or complex scenes. To overcome this, parallelization techniques and distributed computing frameworks can be employed to enhance computational efficiency. Another limitation is the sensitivity to noise and outliers, which can affect the accuracy of the registration results. Implementing robust outlier rejection mechanisms and noise filtering algorithms can help mitigate these issues. Additionally, incorporating adaptive learning rates and regularization techniques in the optimization process can improve convergence and stability in challenging scenarios. Furthermore, exploring hybrid approaches that combine deep learning with traditional optimization methods can potentially address the limitations of the local-global optimization approach and enhance its performance in handling diverse and complex registration tasks.

What other applications beyond localization could benefit from the robust cross-modality registration capabilities of FF-LOGO

Beyond localization, the robust cross-modality registration capabilities of FF-LOGO can benefit various applications in robotics, computer vision, and augmented reality. One potential application is object recognition and classification, where accurate alignment of point clouds from different sensors can improve object detection and identification accuracy. Another application is scene reconstruction and modeling, where precise registration of point clouds can lead to more detailed and realistic 3D models. Furthermore, in autonomous navigation and mapping, the ability to register point clouds from diverse sensors can enhance localization accuracy and map consistency, leading to improved navigation performance. Additionally, in virtual reality and simulation, the registration framework can be utilized for creating immersive and interactive virtual environments by aligning virtual objects with real-world scenes accurately. Overall, the versatility of FF-LOGO in handling cross-modality point cloud registration opens up opportunities for a wide range of applications beyond just localization.