Visual Inertial Odometry (VIO) is the problem of estimating a robot's trajectory by combining information from an inertial measurement unit (IMU) and a camera, and is of great interest to the robotics community. This paper develops a novel Lie group symmetry for the VIO problem and applies the recently proposed equivariant filter. The symmetry is shown to be compatible with the invariance of the VIO reference frame, lead to exact linearisation of bias-free IMU dynamics, and provide equivariance of the visual measurement function. As a result, the equivariant filter (EqF) based on this Lie group is a consistent estimator for VIO with lower linearisation error in the propagation of state dynamics and a higher order equivariant output approximation than standard formulations. Experimental results on the popular EuRoC and UZH-FPV datasets demonstrate that the proposed system outperforms other state-of-the-art VIO algorithms in terms of both speed and accuracy.
翻译:视觉不连续测量( VIO) 是结合惯性测量单位和相机的信息来估计机器人轨迹的问题, 机器人界对此非常感兴趣。 本文为VIO 问题开发了一个新颖的 Lie 群群对称, 并应用了最近提议的等离差过滤器。 对称显示与VIO 参考框架的偏差兼容, 导致无偏差的IMU动态的精确线性化, 并为视觉测量功能提供了等同性。 因此, 以这个 Lie 组为基础的等差过滤器( EqF) 是 VIO 的一致的估测器, 在州动态的传播中具有较低的线性误差, 并且比标准配方的更高排序等离值输出近度。 流行的 EuRoC 和 UZH- FPV 数据集的实验结果显示, 拟议的系统在速度和精确性VO 方面均优于其他状态的VIO 算法 。