Monocular Visual-Inertial-Wheel Odometry Using Low-Grade IMU in Urban Areas

In this article, we propose a new methodology to fuse visual-inertial measurements for land vehicles in a challenging urban environment in which a GNSS signal is not available nor reliable. Motivated by a degenerate case caused by a large bias of a MEMS IMU, we redesign a system model of visual-inertial odometry in a framework of extended Kalman filter. In particular, the system model is propagated through a reduced inertial sensor system composed of a 3-axis gyroscope, a 2-axis accelerometer, and a single-axis odometer. An analytical observability derivation reveals unobservable bases of our estimator, and these directions are resolved by using intermittent position measurements from a GNSS receiver. Furthermore, we inspect the uncertainties of the state vector in a Monte-Carlo simulation that agrees with our theoretical results. The proposed method is validated through the KITTI benchmark dataset and an extensive field testing showing a position drift as 1.25% in tunnels on average and a mean position error of 2.81m in the street canyon over a 6.7km driving.