论文标题
fej-viro：一致的一致的雅各布式视觉惯性探针仪
FEJ-VIRO: A Consistent First-Estimate Jacobian Visual-Inertial-Ranging Odometry
论文作者
论文摘要
近年来，视觉惯性进程（VIO）取得了许多重大进展。但是，VIO方法遭受长期轨迹的定位漂移。在本文中，我们提出了一个第一估计雅各布式视觉惯性范围的探光仪（FEJ-VIRO），以减少VIO的本地化漂移，通过将超宽带（UWB）范围的测量纳入VIO框架\ TextIt {Cysecy}}中。考虑到UWB锚的初始位置通常不可用，我们提出了一个长短的窗口结构，以初始化UWB锚位置以及州增强的协方差。初始化后，FEJ-VIRO与机器人姿势同时估算了UWB锚定位的位置。 We further analyze the observability of the visual-inertial-ranging estimators and proved that there are \textit{four} unobservable directions in the ideal case, while one of them vanishes in the actual case due to the gain of spurious information.基于这些分析，我们利用FEJ技术来强制执行不可观察的方向，从而减少了估计器的不一致。最后，我们通过模拟和现实世界实验验证分析并评估所提出的FEJ-VIRO。
In recent years, Visual-Inertial Odometry (VIO) has achieved many significant progresses. However, VIO methods suffer from localization drift over long trajectories. In this paper, we propose a First-Estimates Jacobian Visual-Inertial-Ranging Odometry (FEJ-VIRO) to reduce the localization drifts of VIO by incorporating ultra-wideband (UWB) ranging measurements into the VIO framework \textit{consistently}. Considering that the initial positions of UWB anchors are usually unavailable, we propose a long-short window structure to initialize the UWB anchors' positions as well as the covariance for state augmentation. After initialization, the FEJ-VIRO estimates the UWB anchors' positions simultaneously along with the robot poses. We further analyze the observability of the visual-inertial-ranging estimators and proved that there are \textit{four} unobservable directions in the ideal case, while one of them vanishes in the actual case due to the gain of spurious information. Based on these analyses, we leverage the FEJ technique to enforce the unobservable directions, hence reducing inconsistency of the estimator. Finally, we validate our analysis and evaluate the proposed FEJ-VIRO with both simulation and real-world experiments.
