YGDD‐SLAM: Direct Geometric Constraint SLAM Based on Object Detection and Depth Image Segmentation

ABSTRACT Most existing vision‐based simultaneous localization and mapping systems and their variants still assume that the observation is absolutely static and cannot work well in dynamic environments. In this paper, we propose a direct geometrically constrained SLAM method based on target detection and depth image segmentation, named YGDD‐SLAM. The YGDD‐SLAM system can work robustly, accurately, and continuously in highly dynamic environments. The method first acquires static and potential dynamic feature points in the current frame through a target detection network. Then, dynamic targets are identified by combining the geometric change relationship between static and potential dynamic feature points between adjacent frames. To improve the accuracy of the dynamic judgment, the motion probability of the potential dynamic target in the past few frames is also used for judgment. Subsequently, the dynamic object regions at the pixel level are segmented out based on the double‐peak feature of the gray‐scale histogram of the dynamic target region in the depth image, which ultimately achieves the accurate deletion of all dynamic features points. Meanwhile, we validate YGDD‐SLAM on TUM data set and Bonn data set and prove that it significantly improves the localization accuracy and system stability in different types of dynamic environments.