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Six-Point Method for Multi-Camera Systems with Reduced Solution Space (2402.18066v2)

Published 28 Feb 2024 in cs.CV

Abstract: Relative pose estimation using point correspondences (PC) is a widely used technique. A minimal configuration of six PCs is required for two views of generalized cameras. In this paper, we present several minimal solvers that use six PCs to compute the 6DOF relative pose of multi-camera systems, including a minimal solver for the generalized camera and two minimal solvers for the practical configuration of two-camera rigs. The equation construction is based on the decoupling of rotation and translation. Rotation is represented by Cayley or quaternion parametrization, and translation can be eliminated by using the hidden variable technique. Ray bundle constraints are found and proven when a subset of PCs relate the same cameras across two views. This is the key to reducing the number of solutions and generating numerically stable solvers. Moreover, all configurations of six-point problems for multi-camera systems are enumerated. Extensive experiments demonstrate the superior accuracy and efficiency of our solvers compared to state-of-the-art six-point methods. The code is available at https://github.com/jizhaox/relpose-6pt

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Summary

  • The paper presents novel minimal solvers that perform 6DOF relative pose estimation using just six point correspondences in multi-camera systems.
  • The work establishes ray bundle constraints that substantially reduce the solution space and enhance numerical stability.
  • Extensive experiments validate the method’s superior accuracy and efficiency over state-of-the-art approaches on synthetic and real-world datasets.

Comprehensive Analysis of Six-Point Methods for Multi-Camera Systems Relative Pose Estimation

Introduction

Relative pose estimation within multi-camera systems is a fundamental research area in computer vision with applications spanning autonomous driving, augmented reality, and robotics. The quest to improve the efficiency, accuracy, and stability of relative pose estimation algorithms remains at the forefront of geometric vision research. This paper introduces a novel approach to the six-point method specifically tailored for multi-camera systems, presenting a more accurate and efficient solution to the 6DOF relative pose estimation problem.

Main Contributions

The paper presents significant contributions to the field of relative pose estimation for multi-camera systems, highlighting three primary advancements:

  • Minimal Solvers for Multi-Camera Systems: The work introduces minimal solvers for 6DOF relative pose estimation that require only six point correspondences (PCs) for multi-camera systems. It encompasses a generic solver for generalized cameras and two specialized solvers catering to popular configurations of two-camera rigs.
  • Ray Bundle Constraints: The paper identifies and proves ray bundle constraints when a subset of PCs relate the same cameras across two views, which is instrumental in reducing the solution space and enhancing the numerical stability of the solvers.
  • Enumeration of Six-Point Problems: An exhaustive enumeration of all configurations of minimal six-point problems for multi-camera systems is presented, providing a comprehensive framework for future research in this area.

Related Work

This research builds upon an extensive foundation of previous works aimed at solving relative pose estimation problems. The literature review clearly delineates between single and multi-camera systems, minimal and non-minimal solvers, and linear solutions. This background sets the stage for understanding the novelty and significance of the presented solvers.

Methodology

The methodology section elaborates on the formulation of geometric constraints and relative pose parameterization using Cayley or quaternion formulations. It further describes the construction of an equation system that incorporates both rotation and translation parameters. By leveraging the ray bundle constraints and utilizing the hidden variable technique, the paper provides a detailed account of constructing numerically stable and efficient solvers.

Experimental Evaluation

Extensive experiments demonstrate the superior performance of the proposed solvers against state-of-the-art methods in terms of accuracy and efficiency. The evaluation encompasses synthetic data tests, showcasing the solvers' robustness to noise and their performance under varying configurations, and real-world data experiments using datasets like KITTI, nuScenes, and EuRoC, where the proposed methods outperformed comparative solutions.

Implications and Future Directions

The research presented in this paper has both theoretical and practical implications for the development of advanced relative pose estimation algorithms. It opens up new avenues for further exploration into efficient and accurate minimal solvers for complex multi-camera systems. Future work could explore extending the proposed framework to accommodate partially calibrated camera systems and incorporate motion priors, which would be particularly beneficial in dynamic environments.

Conclusion

By introducing a novel set of minimal solvers and proving the efficacy of ray bundle constraints, this paper significantly contributes to the field of relative pose estimation for multi-camera systems. The detailed enumeration of six-point problems offers a robust framework for future research. The paper's empirical evaluations validate the proposed methods' superiority in accuracy and computational efficiency, promising substantial advancements in real-world applications.

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