论文标题
通过光流辅助惯性导航的运动跟踪
Movement Tracking by Optical Flow Assisted Inertial Navigation
论文作者
论文摘要
在便携式设备上进行稳健，准确的六个自由度跟踪仍然是一个具有挑战性的问题，尤其是在小型手持设备（例如智能手机）上。为了提高鲁棒性和准确性，IMU和相机的互补运动信息通常融合在一起。传统的视觉惯性方法将信息从IMUS融合，并与设备摄像头跟踪的稀疏特征点云。我们考虑了一种视觉密集的方法，其中IMU数据与从相机数据估算的密集光流场融合在一起。应用于完整图像框架的基于学习的方法可以利用流场的视觉线索和全局一致性来改善流量估计。我们展示了如何将基于学习的光流模型与常规惯性导航结合在一起，以及概率深度学习的想法如何有助于测量更新的鲁棒性。 iPad在充满挑战的低文本环境中获得的现实世界数据中证明了实际的适用性。
Robust and accurate six degree-of-freedom tracking on portable devices remains a challenging problem, especially on small hand-held devices such as smartphones. For improved robustness and accuracy, complementary movement information from an IMU and a camera is often fused. Conventional visual-inertial methods fuse information from IMUs with a sparse cloud of feature points tracked by the device camera. We consider a visually dense approach, where the IMU data is fused with the dense optical flow field estimated from the camera data. Learning-based methods applied to the full image frames can leverage visual cues and global consistency of the flow field to improve the flow estimates. We show how a learning-based optical flow model can be combined with conventional inertial navigation, and how ideas from probabilistic deep learning can aid the robustness of the measurement updates. The practical applicability is demonstrated on real-world data acquired by an iPad in a challenging low-texture environment.
