论文标题
OKVIS2：实时可扩展的视觉惯性大满贯，循环闭合
OKVIS2: Realtime Scalable Visual-Inertial SLAM with Loop Closure
论文作者
论文摘要
强大而准确的状态估计仍然是机器人技术，增强和虚拟现实（AR/VR）的挑战，即使视觉惯性同时定位和映射（VI-SLAM）被商品化。在这里，介绍了一个完整的VI-SLAM系统，特别解决了长期和重复的循环范围的挑战。一系列实验表明，它可以实现，部分优于最先进的开源系统实现的目标。该算法的核心是通过边缘化共同观察的边缘化来创建姿势 - 形式的边缘，在循环闭合时，可以将其流动地转变为具有里程碑标记和观察。该方案包含一个实时估计仪，优化了有界大小的因子图，该图形由观测值，IMU前综合误差项和姿势 - 图形边缘组成 - 它允许在需要时优化较大的环路，重复使用相同的因子毛段。
Robust and accurate state estimation remains a challenge in robotics, Augmented, and Virtual Reality (AR/VR), even as Visual-Inertial Simultaneous Localisation and Mapping (VI-SLAM) getting commoditised. Here, a full VI-SLAM system is introduced that particularly addresses challenges around long as well as repeated loop-closures. A series of experiments reveals that it achieves and in part outperforms what state-of-the-art open-source systems achieve. At the core of the algorithm sits the creation of pose-graph edges through marginalisation of common observations, which can fluidly be turned back into landmarks and observations upon loop-closure. The scheme contains a realtime estimator optimising a bounded-size factor graph consisting of observations, IMU pre-integral error terms, and pose-graph edges -- and it allows for optimisation of larger loops re-using the same factor-graph asynchronously when needed.
