Improved Transformer Instance Segmentation Under Dynamic Occlusion Based VSLAM Algorithm

For traditional SLAM (Simultaneous Localization And Mapping) algorithms, it is difficult to mark occluded objects in dynamic scenes with occlusion, and is impossible to accurately judge the motion state of potential objects as well as the number of feature points after culling dynamic objects is small. This paper proposes a VSLAM algorithm based on improved transformer instance segmentation under dynamic occlusion (ITD-SLAM) in dynamic occlusion scenarios. By designing a multi-attention module, this algorithm guides the model to pay attention to the occluded area, and at the same time improves the relative position encoding to optimize the boundary semantics of occluded objects, and accurately mark potential dynamic objects. In order to reduce the influence of dynamic objects on the positioning accuracy of the SLAM system, the motion state of potential dynamic objects is estimated through three steps of camera pose estimation, object motion estimation and object motion judgment, and dynamic objects are eliminated. According to the grid flow motion model, the static background of the culled area is completed, and the feature points of the repair area are screened and repaired by information entropy, and the high-quality feature points are supplemented for camera pose estimation. Experimental results on the public datasets show that this algorithm has better composition ability with its root mean square error reduced by 22.94% when compared with DynaSLAM. © 2023 Chinese Institute of Electronics. All rights reserved.