论文标题
CTRL-VIO：滚动快门摄像头连续的视觉惯性进程
Ctrl-VIO: Continuous-Time Visual-Inertial Odometry for Rolling Shutter Cameras
论文作者
论文摘要
在本文中，我们提出了用于滚动快门摄像机的概率连续时间视觉惯性探测器（VIO）。连续的时轨迹公式自然促进了异步高频IMU数据和运动延伸的滚动快门图像的融合。为了防止棘手的计算负载，提出的VIO是滑动窗口和基于密钥帧的。我们建议将控制点边缘化，以保持滑动窗口中的恒定密钥帧数量。此外，可以在我们的连续时间VIO中在线校准滚动快门相机的线曝光时间差（线延迟）。为了广泛检查我们连续的VIO的性能，对公共可用的WHU-RSVI，TUM-RSVI和Sensetime-RSVI滚动快门数据集进行了实验。结果表明，提出的连续时间VIO显着胜过现有的最新VIO方法。本文的代码库也将通过\ url {https://github.com/april-zju/ctrl-vio}开源。
In this paper, we propose a probabilistic continuous-time visual-inertial odometry (VIO) for rolling shutter cameras. The continuous-time trajectory formulation naturally facilitates the fusion of asynchronized high-frequency IMU data and motion-distorted rolling shutter images. To prevent intractable computation load, the proposed VIO is sliding-window and keyframe-based. We propose to probabilistically marginalize the control points to keep the constant number of keyframes in the sliding window. Furthermore, the line exposure time difference (line delay) of the rolling shutter camera can be online calibrated in our continuous-time VIO. To extensively examine the performance of our continuous-time VIO, experiments are conducted on publicly-available WHU-RSVI, TUM-RSVI, and SenseTime-RSVI rolling shutter datasets. The results demonstrate the proposed continuous-time VIO significantly outperforms the existing state-of-the-art VIO methods. The codebase of this paper will also be open-sourced at \url{https://github.com/APRIL-ZJU/Ctrl-VIO}.
