SLAMBased SelfCalibration of a Binocular Stereo Vision Rig in RealTime The calibration problem of binocular stereo vision rig is critical for its practical application. However, most existing calibration methods are based on manual offline algorithms for specific reference targets or patterns. In this paper, we propose a novel simultaneous localization and mapping (SLAM)based selfcalibration method designed to achieve realtime, automatic and accurate calibration of the binocular stereo vision (BSV) rigxe2x80x99s extrinsic parameters in a short period without auxiliary equipment and special calibration markers, assuming the intrinsic parameters of the left and right cameras are known in advance. The main contribution of this paper is to use the SLAM algorithm as our main tool for the calibration method. The method mainly consists of two parts: SLAMbased construction of 3D scene point map and extrinsic parameter calibration. In the first part, the SLAM mainly constructs a 3D feature point map of the natural environment, which is used as a calibration area map. To improve the efficiency of calibration, a lightweight, realtime visual SLAM is built. In the second part, extrinsic parameters are calibrated through the 3D scene point map created by the SLAM. Ultimately, field experiments are performed to evaluate the feasibility, repeatability, and efficiency of our selfcalibration method. The experimental data shows that the average absolute error of the Euler angles and translation vectors obtained by our method relative to the reference values obtained by Zhangxe2x80x99s calibration method does not exceed 0.5xcbx9a and 2 mm, respectively. The distribution range of the most widely spread parameter in Euler angles is less than 0.2xcbx9a while that in translation vectors does not exceed 2.15 mm. Under the general texture scene and the normal driving speed of the mobile robot, the calibration time can be generally maintained within 10 s. The above results prove that our proposed method is reliable and has practical value.

Degeneracy in SelfCalibration Revisited and a Deep Learning Solution for Uncalibrated SLAM Selfcalibration 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 twoview geometry is used to selfcalibrate 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 checkerboardfree calibration tool for SLAM, achieving comparable or superior performance to previous learning and handcrafted methods.