Direct Sparse Stereo Visual-Inertial Global Odometry

Abstract

Robust and accurate localization plays a key role in autonomous driving and robot applications. To utilize the complementary properties of different sensors, we present a novel tightly-coupled approach to combine the local (stereo cameras, IMU) and global sensors (magnetometer, GNSS). We jointly optimize all the model parameters through one active window. The visual part integrates constraints from static stereo into the photometric bundle adjustment pipeline of dynamic multiview stereo. Accumulating IMU information between keyframes, magnetometer and GNSS measurements are all inserted into the active window as additional constrains among all the keyframes. Through these, our method can realize globally drift-free and locally accurate state estimation. We evaluate the effectiveness of our system on public datasets under with real-world experiments.