To achieve accurate pose estimation of a catadioptric panoramic camera in complex dynamic environments， this study proposed a binocular catadioptric panoramic camera simultaneous localization and mapping （SLAM） system for dynamic environments based on an inverse depth filter. The binocular catadioptric panoramic camera system was analyzed， and the depth information of space points was calculated through the process of triangulation on a sphere. An inverse depth filter based on Bayesian filtering was used to eliminate dynamic map points， whereby only static map points were then used to estimate the poses of the camera. Accurate pose estimation results were obtained in dynamic environments. More specifically， experiments were conducted in both low and high dynamic environments， where results showed that when the trajectory calculated by traditional SLAM system drifted， the dynamic SLAM system based on inverse depth filtering was always executed in a stable manner. The root mean square errors of the absolute trajectory error of our method in the two groups of highly dynamic environments are 0.52 m and 0.56 m， respectively. The accuracy is up to 96.35% higher than that of the traditional panoramic SLAM system.