Degeneracy in Self-Calibration Revisited and a Deep Learning Solution for Uncalibrated SLAM

Publication Date: 11/3/2019

Event: IROS 2019, The Venetian Macao, Macau, China

Reference: pp 3766-3773, 2019

Authors: Bingbing Zhuang, National University of Singapore, NEC Laboratories America, Inc.; Quoc-Huy Tran, NEC Laboratories America, Inc.; Loong Fah Cheong, National University of Singapore; Manmohan Chandraker, NEC Laboratories America, Inc.

Abstract: 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 method

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