VINet: Visual-Inertial Odometry as a Sequence-to-Sequence Learning Problem
Ronald Clark, Sen Wang, Hongkai Wen, Andrew Markham, Niki Trigoni · Warwick Research Archive Portal (University of Warwick) · 2017
In this paper we present VINet - a sequence-to-sequence learning approach to motion estimation using visual and inertial sensors. It is to the best of our knowledge the first end-to-end trainable method for monocular visual- inertial odometry. Our method has numerous advantages over traditional approaches. Specifically, it eliminates the need for tedious manual synchronization of the camera and IMU as well as eliminating the need for manual calibration between the IMU and camera. A further advantage is that our model naturally and elegantly incorporates domain specific information which significantly mitigates drift. We show that our approach is competitive with state-of-the-art traditional methods when accurate calibration data is available and can be trained to outperform them in the presence of calibration and synchronization errors.