SPP-SLAM: Dynamic Visual SLAM with Multiple Constraints based on Semantic Masks and Probabilistic Propagation

Visual simultaneous localization and mapping (vS-LAM) has attracted great attentions in mobile robots. Most vSLAM systems assume that the objects are stationary in static environments. However, in the real world, there are many objects that are non-stationary in dynamic environments, which will cause performance degradation of vSLAM systems. In this paper, we propose a novel vSLAM system suitable for dynamic environments, named as SPP-SLAM, which is based on semantic masks and probabilistic propagation. Prior motion probabilities of feature points are obtained using semantic mask constraints and multi-view geometric constraints. Then the dynamic probability of each feature point is obtained via the probability propagation model, and highly dynamic feature points are rejected. In addition, we propose a missed detection compensation module combined with inertial measurement unit (IMU) information to recover the semantic masks of the missed objects. Experimental results on the OpenLORIS-Scen and TUM RGB-D datasets demonstrate that the proposed approach can improve the performance of vSLAM systems in a variety of challenging scenarios.