This paper is devoted to the construction of a visual-inertial odometry system for an unmanned vehicle using both binocular cameras and inertial sensors as an information source, which would be able to simultaneously determine the vehicle's own position and the relative position of other road users. To ensure accurate and continuous localization, it is proposed to use an inertial navigation system and two types of image keypoints. Deep learning models are used to accurately and reliably track keypoints. To achieve efficient and reliable matching of objects between two frames, a multi-level data association mechanism is proposed that takes into account possible errors of various system components. The experimental results demonstrate the feasibility and application potential of the proposed system.

Keywords: multi-object visual-inertial odometry, localization, data association, tracking of 3D dynamic objects

This paper is devoted to the construction of a robust visual-inertial odometry system for an unmanned vehicle using binocular cameras and inertial sensors as information sources.The system is based on a modified structure of the VINS-FUSION system. Two types of feature points and matching methods are used to better balance the quantity and quality of tracking points. To filter out incorrect matches of key points, it is proposed to use several different methods. Semantic and geometric information are combined to quickly remove dynamic objects. Keypoints of static objects are used to complement the tracking points. A multi-layer optimization mechanism is proposed to fully utilize all point matchings and improve the accuracy of motion estimation. The experimental results demonstrate the effectiveness of the system.

Keywords: robust visual-inertial odometry, localization, road scene, multi-level optimization mechanism