Leveraging Image-based Localization for Infrastructure-based Calibration of a Multi-camera Rig

Most existing calibration methods for multi-camera rigs are computationally expensive, use installations of known fiducial markers, and require expert supervision. We propose an alternative approach called infrastructure-based calibration that is efficient, requires no modification of the infrastructure (or calibration area), and is completely unsupervised. In infrastructure-based calibration, we use a map of a chosen calibration area and leverage image-based localization to calibrate an arbitrary multi-camera rig in near real-time. Due to the use of a map, before we can apply infrastructure-based calibration, we have to run a survey phase once to generate a map of the calibration area. In this survey phase, we use a survey vehicle equipped with a multi-camera rig and a calibrated odometry system, and SLAM-based self-calibration to build the map which is based on natural features. The use of the calibrated odometry system ensures that the metric scale of the map is accurate. Our infrastructure-based calibration method does not assume an overlapping field of view between any two cameras, and does not require an initial guess of any extrinsic parameter. Through extensive field tests on various ground vehicles in a variety of environments, we demonstrate the accuracy and repeatability of the infrastructure-based calibration method for calibration of a multi-camera rig. The code for our infrastructure-based calibration method is publicly available as part of the CamOdoCal library at https://github.com/hengli/camodocal.