Semantic visual SLAM system based on image enhancement and adaptive thresholding
Visual Simultaneous Localization and Mapping(SLAM)is an important component of unmanned mobile systems.However,the technology is currently plagued by localization failures under complex lighting environments with insufficient lighting and uneven lighting,and dynamic environment with moving object interference.To improve the performance of visual SLAM in the aforementioned working environment,a visual SLAM system called Histo-gram equalization and Adaptive threshold SLAM system combined with YOLO(HAYolo-SLAM)was proposed.The system was improved on the basis of ORB-SLAM3,using image enhancement technology based on histogram equalization and a combination of adaptive threshold and dual threshold feature point extraction method in feature point extraction methods.An object detection thread had been added to the visual front-end,giving the sys-tem the ability to obtain semantic information for feature point removal and filtering.Experiments were conducted in different difficult environments,and the experimental results showed that the system could meet the application re-quirements in variable lighting,weak lighting and uneven lighting environments,and could improve the positioning accuracy in dynamic environments.
oriented fast and rotated brief-simultaneous localization and mapping algorithmfeature point extractiondynamic environmentcomplex lighting environmentsimage enhancement
万方数据
维普
