A Tightly Coupled Feature-Based Visual-Inertial Odometry With Stereo Cameras

Early works have shown that inertial measurement unit (IMU) can help visual odometry to achieve more accurate pose estimation. However, existing methods mainly focus on fusing visual and inertial information in the back end, while ignoring it in the front end. In this article, we present a novel feature-based visual-inertial odometry for stereo cameras, namely FSVIO, which makes full use of visual and inertial information in both the front and the back end. Specifically, we first introduce an IMU-aided feature-based method in the visual processing part of the front end, in which IMU information is used to build robust descriptors for image perspective deformation caused by the camera motion. This differs from the traditional feature-based methods that only use local image information in the descriptor construction. Then, in order to improve the efficiency of feature matching, we apply a fast-tracking method by predicting the position of feature points in the current frame with the help of combining stereo camera and IMU measurements, which also reduces outliers caused by dynamic environment or nonconvexity of the image. Furthermore, the 2D–2D epipolar geometry constraint and the improved Huber norm are introduced into the tightly coupled optimization of the back end, which reduces the influence of incorrect depth estimation from stereo cameras. Finally, our odometry is evaluated on both EuRoC datasets and real-world experiments. The experimental results verified the effectiveness and superiority of FSVIO.