基于目标检测和多视图几何的动态SLAM算法  

Dynamic SLAM algorithm based on object detection and multi-view geometry

作　　者：喻擎苍 董根阳 方才威 孙树森[1] YU Qingcang;DONG Genyang;FANG Caiwei;SUN Shusen(School of Computer Science and Technology(School of Artificial Intelligence),Zhejiang Sci-Tech University,Hangzhou 310018,China)

机构地区：[1]浙江理工大学计算机科学与技术学院(人工智能学院),浙江杭州310018

出　　处：《现代电子技术》2025年第1期135-143,共9页Modern Electronics Technique

摘　　要：目前大多数的SLAM系统主要针对静态场景,然而,在实际环境中不可避免地存在许多动态对象,这将大大降低算法的鲁棒性和相机的定位精度。针对动态对象造成的轨迹偏差问题,文中提出一种结合目标检测网络和多视图几何结构的动态SLAM算法。首先,基于YOLOv5算法框架,将骨干网络CSPDarkNet-53替换为轻量型L-FPN(Lite-FPN)结构,并使用VOC2007数据集进行预训练。与YOLOv5s原始模型相比,新网络的计算量减少了45.73%,检测速率提高了31.90%;然后,将检测物体划分为高动态对象、中动态对象以及低动态对象,利用多视图几何方法计算阈值,并根据阈值对中高动态对象进行二次检测,以决定是否剔除预测框中的特征点;最后,在TUM数据集上的实验结果显示,该方法在定位精度上平均提升了82.08%,证明了其在准确性方面的显著改进。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.

关 键 词：同步定位与地图构建 动态环境 多视图几何结构 目标检测 特征点 轻量型 

分 类 号：TN911.73-34[电子电信—通信与信息系统] TP391.41[电子电信—信息与通信工程]