Most current research on dynamic visual Simultaneous Localization and Mapping (SLAM) systems focuses on scenes where static objects occupy most of the environment. However, in densely populated indoor environments, the movement of the crowd can lead to the loss of feature information, thereby diminishing the system’s robustness and accuracy. This paper proposes a visual SLAM algorithm for dense crowd environments based on a combination of the ORB-SLAM2 framework and RGB-D cameras. Firstly, we introduced a dedicated target detection network thread and improved the performance of the target detection network, enhancing its detection coverage in crowded environments, resulting in a 41.5% increase in average accuracy. Additionally, we found that some feature points other than humans in the detection box were mistakenly deleted. Therefore, we proposed an algorithm based on standard deviation fitting to effectively filter out the features. Finally, our system is evaluated on the TUM and Bonn RGB-D dynamic datasets and compared with ORB-SLAM2 and other state-of-the-art visual dynamic SLAM methods. The results indicate that our system’s pose estimation error is reduced by at least 93.60% and 97.11% compared to ORB-SLAM2 in high dynamic environments and the Bonn RGB-D dynamic dataset, respectively. Our method demonstrates comparable performance compared to other recent visual dynamic SLAM methods.

目前大多数关于动态视觉同步定位与建图（SLAM）系统的研究都集中在静态物体占据大部分环境的场景。然而，在人口密集的室内环境中，人群的移动会导致特征信息的丢失，从而降低系统的鲁棒性和准确性。本文提出了一种基于ORB-SLAM2框架和RGB-D相机相结合的针对密集人群环境的视觉SLAM算法。首先，我们引入了专用的目标检测网络线程，提高了目标检测网络的性能，增强了其在拥挤环境下的检测覆盖率，平均准确率提高了41.5%。此外，我们发现检测框中除人类以外的一些特征点被错误删除。因此，我们提出了一种基于标准差拟合的算法来有效地过滤掉特征。最后，我们的系统在 TUM 和 Bonn RGB-D 动态数据集上进行评估，并与 ORB-SLAM2 和其他最先进的视觉动态 SLAM 方法进行比较。结果表明，与高动态环境中的 ORB-SLAM2 和波恩 RGB-D 动态数据集相比，我们的系统的位姿估计误差分别降低了至少 93.60% 和 97.11%。与其他最近的视觉动态 SLAM 方法相比，我们的方法表现出了可比的性能。