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.
Monocular Visual-Inertial-Wheel Odometry Using Low-Grade IMU in Urban Areas
IEEE Transactions on Intelligent Transportation Systems ; 23 , 2 ; 925-938
01.02.2022
3777671 byte
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
British Library Conference Proceedings | 2020
|GROUND VEHICLE MONOCULAR VISUAL-INERTIAL ODOMETRY VIA LOCALLY FLAT CONSTRAINTS
Europäisches Patentamt | 2022
|Uncertainty-Aware Attention Guided Sensor Fusion For Monocular Visual Inertial Odometry
Deutsches Zentrum für Luft- und Raumfahrt (DLR) | 2020
|