The existing stereo simultaneous localization and mapping ( SLAM) methods all use standard stereo cameras, and the assumption of the static environment has influence on their accuracy in dynamic environment. A multi-focal dynamic stereo vision SLAM is proposed. It could overcome the insufficiency of standard stereo cameras that cannot perceive the scene at long distance and wide field of view. The impact of dynamic objects is also removed. To be specific, the stereo calibration method is improved and the calibration parameters are utilized to rectify ORB features instead of rectifying stereo images. For multi-focal stereo images, a feature extraction and matching method is also proposed to increase the number of matched features. Multi-view geometry, regional feature flow and relative distance are used to detect dynamic objects. The feature points on the dynamic objects are eliminated. Compared with ORB-SLAM3 and DynaSLAM, the positioning accuracy of the proposed method on the public data set KITTI is increased by 6. 97% , and the positioning accuracy on the self-made data set is increased by 26. 64% and 32. 09% , respectively.