Abstract
This paper presents a tightly coupled Vector Field Inertial Localization (VFIL) method that integrates the capa- bilities of Vector Field Sensors (VFSs) and an Inertial Measure- ment Unit (IMU). While vector fields, such as magnetic or Wi-Fi signal intensities, provide vector data at each point, leveraging VFSs for accurate localization is challenging due to their limited measurement range. Within the VFIL framework, the IMU predicts the pose, and the VFS compensates any prediction errors. To enhance constraints with the VFS, it is essential to store vector data based on the predicted trajectory. While this trajectory can be deduced from accumulated IMU readings, significant error is often included in these accumulations. Our solution is to register the vector data to a vector field map and simultaneously correct the accumulation error. We achieve this by adopting a factor-graph-based optimization method that concurrently estimates pose, velocity, and biases in both IMU and VFS measurements. To demonstrate effectiveness of VFIL, we conduct simulations and real-world experiments, comparing it against particle-filtering and pose-graph-based optimization methods. Results reveal that VFIL consistently offers superior pose estimation accuracy compared to the compared methods owing to the tightly coupled estimation.