论文标题
DM-VIO：延迟边缘化视觉惯性进程
DM-VIO: Delayed Marginalization Visual-Inertial Odometry
论文作者
论文摘要
我们提出了DM-VIO，这是一种基于两种称为延迟边缘化和姿势图形束调节的新型技术的单眼视觉惯性进程系统。 DM-VIO对视觉残留物进行动态重量进行光度束调节。我们采用边缘化，这是一种流行的策略，可以保持更新时间的限制，但不能轻易逆转，并且必须固定连接变量的线性化点。为了克服这一点，我们提出了边缘化的延迟：这个想法是要维持第二个因子图，其中边缘化被延迟。这使我们稍后可以阅读此延迟图，并具有新的和一致的线性化点，从而产生了更新的边缘化。此外，延迟的边缘化使我们能够将IMU信息注入已经边缘化的状态。这是提出的姿势图束调整的基础，我们将其用于IMU初始化。与先前关于IMU初始化的工作相反，它能够捕获完整的光度不确定性，从而改善规模估计。为了应对最初不可观察的规模，在IMU初始化完成后，我们继续优化主系统中的比例和重力方向。我们在Euroc，Tum-VI和4季度数据集上评估了系统，其中包括飞行无人机，大规模手持和汽车场景。多亏了提出的IMU初始化，我们的系统在视觉惯性的探光仪中超过了最新技术，即使仅使用单个相机和IMU，甚至超过了立体惯性方法。该代码将在http://vision.in.tum.de/dm-vio上发布
We present DM-VIO, a monocular visual-inertial odometry system based on two novel techniques called delayed marginalization and pose graph bundle adjustment. DM-VIO performs photometric bundle adjustment with a dynamic weight for visual residuals. We adopt marginalization, which is a popular strategy to keep the update time constrained, but it cannot easily be reversed, and linearization points of connected variables have to be fixed. To overcome this we propose delayed marginalization: The idea is to maintain a second factor graph, where marginalization is delayed. This allows us to later readvance this delayed graph, yielding an updated marginalization prior with new and consistent linearization points. In addition, delayed marginalization enables us to inject IMU information into already marginalized states. This is the foundation of the proposed pose graph bundle adjustment, which we use for IMU initialization. In contrast to prior works on IMU initialization, it is able to capture the full photometric uncertainty, improving the scale estimation. In order to cope with initially unobservable scale, we continue to optimize scale and gravity direction in the main system after IMU initialization is complete. We evaluate our system on the EuRoC, TUM-VI, and 4Seasons datasets, which comprise flying drone, large-scale handheld, and automotive scenarios. Thanks to the proposed IMU initialization, our system exceeds the state of the art in visual-inertial odometry, even outperforming stereo-inertial methods while using only a single camera and IMU. The code will be published at http://vision.in.tum.de/dm-vio
