Degeneracy in Self-Calibration Revisited & a Deep Learning Solution for Uncalibrated SLAM
We first revisit the geometric approach to radial distortion self-calibration and provide a proof that explicitly shows the ambiguity between radial distortion and scene depth under forward camera motion. In view of such geometric degeneracy and the prevalence of forward motion in practice, we further propose a learning approach that trains a convolutional neural network on a large amount of synthetic data to estimate the camera parameters and show its application to SLAM without knowing camera parameters prior.
Degeneracy in Self-Calibration Revisited and a Deep Learning Solution for Uncalibrated SLAM Paper
Self-calibration of camera intrinsics and radial distortion has a long history of research in the computer vision community. However, it remains rare to see real applications of such techniques to modern Simultaneous Localization And Mapping (SLAM) systems, especially in driving scenarios. In this paper, we revisit the geometric approach to this problem, and provide a theoretical proof that explicitly shows the ambiguity between radial distortion and scene depth when two-view geometry is used to self-calibrate the radial distortion. In view of such geometric degeneracy, we propose a learning approach that trains a convolutional neural network (CNN) on a large amount of synthetic data. We demonstrate the utility of our proposed method by applying it as a checkerboard-free calibration tool for SLAM, achieving comparable or superior performance to previous learning and hand-crafted methods.