A great number of visual simultaneous localization and mapping (VSLAM) systems need to assume static features in the environment. However, moving objects can vastly impair the performance of a VSLAM system which relies on the static-world assumption. To cope with this challenging topic, a real-time and robust VSLAM system based on ORB-SLAM2 for dynamic environments was proposed. To reduce the influence of dynamic content, we incorporate the deep-learning-based object detection method in the visual odometry, then the dynamic object probability model is added to raise the efficiency of object detection deep neural network and enhance the real-time performance of our system. Experiment with both on the TUM and KITTI benchmark dataset, as well as in a real-world environment, the results clarify that our method can significantly reduce the tracking error or drift, enhance the robustness, accuracy and stability of the VSLAM system in dynamic scenes.

大量的视觉同步定位和映射 (VSLAM) 系统需要假设环境中的静态特征。然而，移动物体会极大地削弱依赖于静态世界假设的 VSLAM 系统的性能。为了应对这个具有挑战性的话题，提出了一种基于 ORB-SLAM2 的动态环境的实时鲁棒 VSLAM 系统。为了减少动态内容的影响，我们在视觉里程计中加入了基于深度学习的物体检测方法，然后加入动态物体概率模型，以提高深度神经网络物体检测的效率，增强实时性。我们的系统。在 TUM 和 KITTI 基准数据集以及真实环境中进行实验，