Suting Ma
ABSTRACT
Most of visual synchronous localization and mapping (VSLAM) algorithms are designed based on static scenes and the influence of moving objects in the scene can not be ignored. Due to the presence of moving objects in real scenes, the feature points of the visual odometer is usually easy to be mismatched, which affects the positioning and mapping accuracy of the SLAM system, resulting in the low robustness in practical applications. AVSLAM algorithm based on the ORB-SLAM2 based on deep learning is proposed in this paper, whichoutperforms on identifying and removing dynamic feature points in the tracking procedure of the SLAM system operated in dynamic environments. The local optical flow method is combined to determine the dynamic objects in the environment. In addition, an improved model based on DeepLabv3+ is introduced, which can simultaneously perform object detection and semantic segmentation for accurately removing dynamic feature points. Finally, the accuracy and robustness of the semantic segmentation model and improved algorithms are evaluated after the Cityscapes and TUM datasets are adopted. The experimental results on the TUM datasets show that in high dynamic scenarios, proposed algorithm improves the accuracy of trajectory estimation by 60.69% and 84.91%, respectively compared to ORB-SLAM2 method. The groups of experiments show that the SLAM algorithm based on the improved DeepLabv3+Semantic network model significantly improves the robustness and accuracy of the VSLAM system in the dynamic environment.
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Index Terms
- SLAM system based on improved DeepLabv3+ semantic network model
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