鲁棒单目视觉惯性里程计初始化方法

Robust Initial Methods of Monocular Visual Inertial Odometry

提出了一种鲁棒的单目视觉惯性里程计的初始化方法。视觉惯性里程计是一个非线性系统,求解非线性系统状态估计的问题,需要先得到系统状态量的初始值。不准确的初始值将直接导致整个的状态估计失败。先通过纯视觉数据,根据SFM算法获取缺少尺度信息的相机位姿和特征点位置(运动结构)。然后将该运动结构与经过IMU预积分得到的运动约束融合标定,恢复出尺度因子、初始速度、重力向量以及陀螺仪漂移。该方法可以在不知道系统状态的先验信息的情况下初始化成功。用公开ASL数据集来验证,结果表明该方法初始化成功率很高。

This paper proposes a robust on-the-fly estimator initialization algorithm to provide high-quality initial states for monocular visual-inertial systems(VINS).Due to the non-linearity of VINS,a poor initialization can severely impact the performance of filtering-based or graph-based methods.Our approach starts with a vision-only structure from motion(SfM)to build the up-to-scale structure of camera poses and feature positions.By loosely aligning this structure with pre-integrated IMU measurements,our approach recovers the metric scale,velocity,gravity vector,and gyroscope bias.our approach can perform on-the-fly initialization in various scenarios without using any prior information about system states and movement.