OMC-VINS: A Bionic Skylight Orientation and Vision-Based Metric Constrains Proposal for Vehicle Location in Challenging Environment

With the advancement of the Vehicle-based Internet of Things (IoT), the demand for positioning in challenging environment is growing. As a commonly employed positioning method, satellite navigation faces challenges in occlusion-prone environments, which poses difficulties in meeting the navigational requirements of vehicles. Inspired by insects orienting with the polarized skylight, we developed a bionic polarized compass and integrate it into our vision-based vehicular navigation system, aiming to enhance the performance of satellite systems in the IoT. Correspondingly, we introduce a multisensor fusion framework with polarized orientation and metric constraints visual inertial navigation system, which has capabilities for real-time location in large-scale areas, even in satellite-denied environments. First, we describe an optimization-based framework, which integrates visual, inertial, polarized orientation, and global position to get metric constraints. Subsequently, the visual-inertial system is coupled with metric constraints in practical applications, achieving large-scale drift-free positioning in satellite-denied environments. Finally, we conducted self-driving experiments in the city and walking experiments in the jungle, as well as tested the algorithm on Ford multi-AV seasonal data sets. The results of experiments, which lasted over 16 h and covered a distance of more than 150 km, show that the root mean square error of location is no more 6.18 m (no more than 0.04% $\circ $ of the total mileage). The experiments demonstrate that our method has better accuracy and robustness than existing vision-based methods and can be a useful complement to satellite-based navigation methods in the IoT.