A new calibration technique for multi-camera systems of limited overlapping field-of-views

Abstract

State-of-the-art calibration methods typically choose to use a checkerboard as the calibration target for its simplicity and robustness. They however require the complete checkerboard be captured to break symmetry. More recent multi-camera systems such as Google Jump, Jaunt, and camera arrays have limited overlapping field-of-view (FoV) and having all cameras viewing the complete checkerboard is extremely difficult in reality. Tailored patterns such as CALTag [1] introduce new image features within the checker blocks for breaking symmetry but they also break the grid topology. We present a new technique using such patterned calibration targets for a broad range of multi-camera systems. Our key observation is that applying directional gradient filters yields to heterogeneous responses on grid vs. non-grid features: the former are isolated and the latter are highly inter-connected. We therefore apply a simple but highly efficient technique to eliminate non-grid outliers based on connected component analysis and gradient histograms. Finally, we recover the complete grid by approximating each local checkerboard as a parallelogram and imposing the topology constraint. We conduct comprehensive experiments on a number of recent multi-camera systems and our technique significantly outperforms the state-of-the-art in accuracy and robustness.