动态场景下基于3D多目标追踪的实时视觉SLAM方法研究

Research on Real-Time Visual SLAM Method Based on 3D Multi-Object Tracking in Dynamic Scenes

近年来一些解决动态场景下的SLAM技术被提出,其中SLAM与MOT结合的技术路线不仅可解决动态场景问题,还可以提高系统对周围场景的理解,获得了更大关注。本文介绍了一种高效的实时在线视觉SLAMMOT融合系统,以双目视觉或RGBD作为输入,只须借助2D目标检测网络,便能高效、准确、鲁棒地跟踪相机以及动态目标的位姿,并生成稀疏点云地图。为提高多动态目标追踪的精度与准确度,引入了级联匹配与IOU匹配结合的策略;利用阿克曼转向模型来简化追踪目标的运动,减少求解动态目标位姿所需匹配点的数量;利用因子图将相机与动态目标的追踪结果进行联合优化,同时提高相机、追踪目标的位姿和地图点的精度。最后在KITTI跟踪数据集上与其他方法进行比较。结果表明,在满足实时性要求的前提下,该方法仍能准确地追踪相机以及动态目标位姿。

In recent years,some technologies have emerged to tackle the challenges of Simultaneous Local-ization and Mapping(SLAM)in dynamic scenes,among which the integration of SLAM and moving object tracking(MOT)has gained significant attention as it not only tackles the problem of dynamic scenes but also enhances the system's understanding of the surrounding environment.In this paper,an efficient real-time online visual SLAM-MOT fusion system is introduced in this paper,which takes binocular vision or RGBD as input.With the help of a 2D object detection network,this approach can track the camera and dynamic object poses efficiently,accurately and robustly,while generating a sparse point cloud map.Additionally,to improve the precision and accuracy of multi-dynamic object tracking,a strategy combining the cascaded matching and IOU matching strategy is intro-duced.The Ackermann steering model is used to simplify the motion estimation of the tracked objects to reduce num-ber of matching points required to solve dynamic target pose.By employing a factor graph,the tracking results of both the camera and dynamic objects are jointly optimized,simultaneously enhancing the accuracy of the camera,object poses,and map points.Finally,the proposed method is compared with other approaches using the KITTI tracking dataset.The results show that,while satisfying real-time requirements,this method can still achieve accu-rate camera and dynamic object pose tracking.