LOVINS:Lightweight Omnidirectional Visual-Inertial Navigation System

Visual-inertial navigation system (VINS) is the common system for autonomous positioning and navigation, which consists of a camera and an inertial measurement unit (IMU). However, due to size and cost constraints, it is possible for the system to use only cheap, low performance sensors or processors in some platforms with limited computing resources, thus there are many challenges in terms of algorithm robustness and computational efficiency. For this reason, we developed a lightweight omnidirectional visual-inertial navigation system (LOVINS), which is a navigation system that incorporates wide field of view (FOV) camera and IMU. In order to limit the computational complexity, at the front-end of the system, direct method is used to initialize the system and track non-keyframes for pose estimation, feature-based method is used to track keyframes for back-end nonlinear optimization. While at the back-end, sliding window is used for nonlinear optimization, and marginalization is adopted to fix the number of keyframes and ensure the sparsity, thus reduce the system data redundancy properly. The experiments on TUM VI benchmark demonstrate that, compared with other state-of-the-art methods, LOVINS has a higher performance in accuracy and robustness, especially in real-time, due to the advantages of wide FOV camera and frame tracking strategy.