ROCHADE: Robust Checkerboard Advanced Detection for Camera Calibration

Simon Placht^{1, 2}, Peter Fürsattel^{1, 2}, Etienne Assoumou Mengue², Hannes Hofmann¹, Christian Schaller¹, Michael Balda¹, and Elli Angelopoulou²

¹Metrilus GmbH, Erlangen, Germany

²Pattern Recognition Lab, University of Erlangen, Nuremberg, Germany

Abstract. We present a new checkerboard detection algorithm which is able to detect checkerboards at extreme poses, or checkerboards which are highly distorted due to lens distortion even on low-resolution images. On the detected pattern we apply a surface fitting based subpixel refinement specifically tailored for checkerboard X-junctions. Finally, we investigate how the accuracy of a checkerboard detector affects the overall calibration result in multi-camera setups. The proposed method is evaluated on real images captured with different camera models to show its wide applicability. Quantitative comparisons to OpenCV’s checkerboard detector show that the proposed method detects up to 80% more checkerboards and detects corner points more accurately, even under strong perspective distortion as often present in wide baseline stereo setups.

Keywords: Checkerboard Detection, Saddle-Based Subpixel Refinement, Multi Camera Calibration, Low Resolution Sensors, Lens Distortion

LNCS 8692, p. 766 ff.

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