Most existing dynamic simultaneous localization and mapping (SLAM) algorithms simply remove dynamic objects, resulting in the loss of dynamic object motion information that aids in the system′s own localization and navigation, and have limitations for complex and ever-changing industrial environments. In this paper, we propose an improved visual SLAM algorithm for target tracking that performs localization while obtaining a more accurate estimate of the object′s pose. The algorithm uses background points for its own localization, uses refined optical flow information to reduce the effect of noise for accurate localization, and then combines the scene flow information with polynomial residuals to obtain accurate dynamic object sensing results and to reduce the algorithm′s error in estimating the object′s pose. Finally, the proposed algorithm is evaluated on the publicly available KITTI Tracking dataset and real scenes. The experimental results show that on the public dataset, the proposed algorithm has an average rotation error (RPER) of 0.027° and an average displacement error (RPET) of 0.069 m. The average rotation error of object pose estimation is 0.686 97°, and the average displacement error is 0.103 50 m. The proposed algorithm is able to have a better performance of self-localization and dynamic object tracking. The proposed algorithm also shows excellent localization and tracking performance in real scenarios.