Movement Tracking by Optical Flow Assisted Inertial Navigation

TL;DR

This paper presents a novel dense visual-inertial tracking method that combines optical flow estimated by a learning-based model with inertial navigation to improve robustness and accuracy on portable devices.

Contribution

It introduces a new approach integrating learning-based optical flow with inertial data, enhancing visual-inertial tracking in low-texture environments.

Findings

Effective fusion of optical flow and inertial data improves tracking accuracy.

Demonstrated robustness in low-texture, real-world scenarios.

Utilizes probabilistic deep learning to enhance measurement robustness.

Abstract

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.