The paper presents a continuous-time visual-inertial state estimation algorithm based on Chebyshev polynomial optimization. The key highlights are:
Pose is modeled as a Chebyshev polynomial, with velocity and position obtained through analytical integration and differentiation. This transforms the continuous-time state estimation problem into a constant parameter optimization problem.
The optimization objective function incorporates the original IMU measurements, visual reprojection errors, and initial state constraints, avoiding the linearization issues in filtering methods and preserving the quasi-Gaussian nature of the measurements.
The use of Chebyshev polynomials ensures high accuracy and efficiency in the functional approximation. Simulation and experimental results on public datasets demonstrate that the proposed method outperforms traditional preintegration methods in both accuracy and computational efficiency.
The paper discusses the limitations of the current method, such as the lack of adaptive polynomial order selection and the focus on batch optimization. Future work will address these limitations by developing adaptive and real-time implementations of the Chebyshev polynomial optimization for visual-inertial state estimation.
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by Hongyu Zhang... a las arxiv.org 04-02-2024
https://arxiv.org/pdf/2404.01150.pdfConsultas más profundas