Abstract: To solve the performance degradation problem of the mobile robot 3D V-SLAM (visual simultaneous localization and mapping) in the presence of large viewing angle, an affine invariant features matching algorithm AORB (affine oriented FAST and rotated BRIEF) is proposed, and a mobile robot large viewing angle 3D V-SLAM system using Kinect camera is further developed. Firstly, AORB algorithm is adopted to implement the fast and efficient matching between adjacent frames captured by the Kinect RGB camera in the presence of large changes of viewing angle, and the corresponding relationship between adjacent frames is created. Secondly, 2D image points are converted into 3D color cloud data through using the calibrated intrinsic and extrinsic parameters of Kinect, and pixel depth values after alignment correction. Thirdly, the relative pose between adjacent frames is computed by using the least-squares algorithm after removing outliers using RANSAC (RANdom Sample Consensus). Finally, the 3D model is obtained by optimizing the resulting pose using g²o (general graph optimization). Mobile robot large viewing angle 3D V-SLAM is realized ultimately. Both the off-line (based on well-known and available benchmark data sets) and the online (with a developed mobile robot system) experimental testing show that the proposed matching algorithm and the developed 3D V-SLAM system can accurately update the local model, successfully reconstruct the environment model, and effectively estimate the motion trajectory of the mobile robot in the presence of large viewing angle.