An Accurate and Real-time Relative Pose Estimation from Triple Point-line Images by Decoupling Rotation and Translation (2403.11639v1)

Abstract: Line features are valid complements for point features in man-made environments. 3D-2D constraints provided by line features have been widely used in Visual Odometry (VO) and Structure-from-Motion (SfM) systems. However, how to accurately solve three-view relative motion only with 2D observations of points and lines in real time has not been fully explored. In this paper, we propose a novel three-view pose solver based on rotation-translation decoupled estimation. First, a high-precision rotation estimation method based on normal vector coplanarity constraints that consider the uncertainty of observations is proposed, which can be solved by Levenberg-Marquardt (LM) algorithm efficiently. Second, a robust linear translation constraint that minimizes the degree of the rotation components and feature observation components in equations is elaborately designed for estimating translations accurately. Experiments on synthetic data and real-world data show that the proposed approach improves both rotation and translation accuracy compared to the classical trifocal-tensor-based method and the state-of-the-art two-view algorithm in outdoor and indoor environments.

• The paper introduces RT²PL, a decoupled approach that separately estimates high-precision rotation and robust translation.
• It employs normal vector coplanarity constraints for superior rotation accuracy, effectively overcoming challenges in weak-textured settings.
• Extensive experiments on synthetic and real-world data show marked improvements over traditional and state-of-the-art pose estimation methods.

Advanced Pose Estimation from Triple Point-Line Images with Decoupled Rotation and Translation

Overview

A novel methodology for three-view relative pose estimation, leveraging both point and line features within man-made environments, is proposed. This paper illustrates an approach that significantly enhances the accuracy of pose estimation in scenarios characterized by weak-textured surfaces by utilizing the complementary information provided by line features alongside point features. Unlike existing methodologies that mix rotation and translation parameters, leading to potential inaccuracies and inefficiencies, this research introduces a decoupled estimation of rotation and translation. The proposed algorithm efficiently solves for high-precision rotation through normal vector coplanarity constraints and accurately estimates translations via a uniquely designed robust linear translation constraint. The efficacy of this method has been demonstrated through comprehensive experiments on both synthetic and real-world data, showing marked improvements over classical and state-of-the-art algorithms in various environments.

Key Contributions

• Decoupled Estimation Approach: A significant leap from traditional methods, this research presents RT$^2$PL, a method that decouples the estimation of rotation and translation, addressing the challenges posed by pure rotation and planar degeneracies effectively.
• Normal Vector Coplanarity Constraints for Rotation: High-precision rotation estimation is achieved using coplanarity constraints. Specifically, two forms of constraints are discussed - minimal eigenvalue form and eigenvalue multiplication form, with the latter demonstrating superior convergence and resilience to initial guess inaccuracies.
• Robust Linear Translation Constraint: An innovative translation constraint is introduced, minimizing the impact of rotation estimation errors and observation noise, thus enhancing the accuracy of translation estimates.
• Integration of Point and Line Features: The method capitalizes on the strengths of both point and line features, significantly improving pose estimation accuracy, especially in environments with weak textures where line features prevail.

Practical and Theoretical Implications

The algorithm presents a robust framework for pose estimation that could significantly benefit Visual Odometry (VO) and Structure-from-Motion (SfM) systems, especially in man-made environments characterized by weak textures. By providing a more accurate initial pose using decoupled rotation and translation estimates, the research paves the way for more reliable and efficient VO and SfM systems that can function effectively even in challenging conditions. Furthermore, the insights gained from the decoupled estimation approach and the integration of point-line features offer promising directions for future research in enhancing pose estimation methodologies.

Speculations on Future Developments

The successful application of RT$^2$PL in enhancing accuracy underlines the potential of integrating additional types of features and employing this approach in more complex scenarios, such as dynamic environments. Future studies could explore the extension of this methodology to Visual-Inertial Odometry (VIO) systems, potentially leading to significant advancement in autonomous navigation technologies, including drones and autonomous vehicles operating in urban settings. Moreover, further examination into optimizing the computational efficiency of the method could facilitate its application in real-time systems that require rapid pose estimation.

Conclusion

The RT$^2$PL method introduced in this paper marks a significant advancement in the field of pose estimation, providing a novel solution that accurately and efficiently solves the three-view relative pose estimation problem by decoupling the rotation and translation estimates. The algorithm not only demonstrates superior performance in terms of accuracy and efficiency compared to existing methods but also opens promising avenues for future research and developments in advanced pose estimation techniques.