Robust Neural Visual Inertial Odometry With Deep Velocity Constraint

Visual inertial odometry (VIO) is a popular solution for localization in the GPS-denied environment. However, current VIO algorithms' performance degrades in the visual degraded environment due to limited visual features. This paper presents a robust VIO algorithm with learned velocity constraints. In particular, an encoder-decoder neural network augmented with the attention mechanism is employed to estimate the instant velocity and its uncertainty based on the IMU measurement. Then the learned velocities serve as the pseudo-measurement and fused with the inertial and visual measurement within a standard optimization framework to obtain an accurate and robust localization result. The key idea of this work is that the velocity constraint learned from the inertial measurement can effectively prevent the divergence of the VIO in the visual degraded environment without introducing any new sensors and better model the short-term characteristics of the motion compared to the previous neural VIO algorithms based on the displacement vector. Experiments on the public dataset demonstrate that the proposed VIO algorithm operates robustly under the low-visibility condition and obtains higher localization accuracy compared with other state-of-the-art algorithms. We will share code and models.