Dynamic SLAM algorithm based on object detection and multi-view geometry
Nowadays,most SLAM(simultaneous localization and mapping)systems mainly focus on static scenes.However,there are many dynamic objects inevitably in the real environment,which will greatly reduce the robustness of the algorithm and the positioning accuracy of the camera.Therefore,a dynamic SLAM algorithm combining object detection network and multi-view geometric structure is proposed to get rid of the trajectory deviation caused by dynamic objects.On the basis of the framework of YOLOv5 algorithm,the backbone network CSPDarkNet-53 is replaced with a lightweight L-FPN(lightweight feature pyramid network)structure,and the dataset VOC2007 is used for pre-training.The parameters of the proposed network is reduced by 45.73%,and its detection rate is increased by 31.90%in comparison with those of the original model YOLOv5s.Then,the detected objects are categorized into high dynamic objects,medium dynamic objects and low dynamic objects.The multi-view geometric method is used to calculate the threshold value,and the medium and high dynamic objects are detected twice based on the threshold value,so as to decide whether to eliminate the feature points in the prediction frame.The experimental results on the dataset TUM show that the positioning accuracy of the proposed method is improved by 82.08%on average,demonstrating significant improvement in accuracy.
万方数据
维普
