Towards Autonomous Auto Calibration of Unregistered RGB-D Setups: The Benefit of Plane Priors

Abstract

In the last few years novel color and depth (RGB-D) sensors have greatly pushed robot perception. To enable a precise pixel-wise fusion of color and depth information good calibration is needed. The calibration determines the intrinsic parameters, the extrinsic parameters, and corrects for depth errors. While classic calibration approaches involve a dedicated calibration target and a trained expert, the autonomous calibration of such camera systems for robots operating in unknown environments is still an open problem. It demands for robust methods that do not need an expert to set up or tune the algorithm. Hence, we present a robust calibration algorithm that utilizes structure from motion (SfM) reconstructions as a calibration target and incorporates plane priors in the optimization to improve the convergence behavior and improve the calibration robustness. We evaluate our method against the state of the art performing over 300 experiments on ten different datasets, and show a significant improvement of the calibration accuracy.