Visual-Inertial Odometry aided by Speed and Steering Angle Measurements

Visual-inertial navigations systems (VINS) can be an essential building block for autonomous systems, that are equipped with a camera and an inertial measurement unit (IMU), especially in indoor or GNSS-denied environments. One prevalent visual-inertial odometry (VIO) algorithm is the multi-state constraint Kalman Filter (MSCKF). OpenVINS is an open source project that performs visual-inertial state estimation using the core functionality of the MSCKF. However, it offers several extensions. It is designed for general purpose, but in certain cases it is beneficial to use domain knowledge in order to increase accuracy and robustness in state estimation. In this paper, we propose using vehicle speed, steering angle, and wheel speeds measurements in conjunction with OpenVINS by performing additional filter updates in the context of autonomous driving. The updates are conducted using a classical Kalman filtering approach, where speed and steering measurements are processed at their respective sensor frequency. Additionally, all measurements between camera frames, which have the slowest measurement frequency, are gathered and a 3 degrees of freedom (DOF) planar motion update is performed in a preintegrated fashion. In contrast to handheld devices and drones commonly used as visual-inertial platforms, the movement of automotive vehicles is constrained in a nonholonomic way which is reflected in the updates. These extensions are evaluated on real-world datasets of typical urban driving. The results show that state estimation with the additional updates is smoother and leads to lower translation errors, where a preintegrated vehicle update using a single-track model offers the best overall performance. The code is provided in a fork of the original project: https://github.com/aserbremen/open_vins