Stereo Visual Inertial Pose Estimation Based on Feedforward and Feedbacks
Shengyang Chen, Yurong Feng, Chih‐Yung Wen, Yajing Zou, Wu Chen
Abstract
In this article, we present a stereo visual inertial pose estimation method based on feedforward and feedbacks. Compared to the widely used filter-based or optimization-based approaches, the proposed method only stores the most recent pose and measurements and thus can achieve fast processing. A gradient decreased feedback, a roll-pitch feedforward, and a bias estimation feedback, are introduced to fuse the vision and the inertial measurements. This system, which is called feedforward and feedback based visual inertial system (FVIS), is evaluated on the popular European robotics challenge micro aerial vehicle (EuRoC MAV) dataset. FVIS achieves high accuracy and robustness with respect to existing visual inertial simultaneous localization and mapping (SLAM) approaches. FVIS has also been implemented and tested on a unmanned aerial vehicle (UAV) platform. The source code developed during this study is available publicly.