Paper ID: 2404.01150

Visual-inertial state estimation based on Chebyshev polynomial optimization

Hongyu Zhang, Maoran Zhu, Qi Cai, Yuanxin Wu

This paper proposes an innovative state estimation method for visual-inertial fusion based on Chebyshev polynomial optimization. Specifically, the pose is modeled as a Chebyshev polynomial of a certain order, and its time derivatives are used to calculate linear acceleration and angular velocity, which, along with inertial measurements, constitute dynamic constraints. This is coupled with a visual measurement model to construct a visual-inertial bundle adjustment formulation. Simulation and public dataset experiments show that the proposed method has better accuracy than the discrete-form preintegration method.

Submitted: Apr 1, 2024