MOD-SLAM:Visual SLAM with Moving Object Detection in Dynamic Environments

In recent years, the signihcant progress has been made in visual simultaneous localization and mapping(VSLAM). Many present geometric VSLAM systems rely on static and bright environment assumptions, which is not friendly to the generalization of VSLAM in the real world including a large number of challenging scenes. To cope with these challenges, a real-time and robust visual-inertial SLAM system was proposed, which integrates a neural network for moving object detection(MOD) and greatly reduces the negative influence of dynamic objects. We has performed an ablation study to validate the effectiveness and necessity of our proposal. In addition, empirical evaluations on typical datasets, as well as in some usual dynamic environments, show that our novel framework can favorably solve the tracking loss, yield pure point cloud and improve the accuracy of VSLAM.