Dynamic Visual SLAM Algorithm Based on Improved YOLOv8s
Aiming at the problem of feature matching performance of SLAM system is reduced due to fast moving objects in dynamic scenes,this paper proposes a dynamic visual SLAM algorithm based on improved YOLOv8s on ORB-SLAM2 framework.The YOLOv8s core network is replaced with lightweight network Fasternet,and the detection head is improved with Transformer Decoder Head in RT-DETR.Combining with geometric and semantic information,the dynamic feature points are eliminated efficiently.Experiments on TUM data set show that,the localization and mapping accuracy of this algorithm in dynamic scenes increase by about 96.06%compared with ORB-SLAM2,and it has good real-time performance.
SLAMtarget detectiondynamic feature point eliminationlocalization and mapping accuracy
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