# DI-SLAM: A Real-Time Enhanced RGB-D SLAM for Dynamic Indoor Environments
###### Abstract  
Common visual simultaneous localization and mapping systems are built on the static environment hypothesis and fail to handle the substantial environmental dynamics. Particularly in highly dynamic environments, the pose estimation errors tend to accumulate rapidly, even causing the system to fail. To mitigate this limitation, we have developed DI-SLAM, an enhanced real-time SLAM system for dynamic indoor environments, extending the capabilities of ORB-SLAM3. DI-SLAM introduces a new parallel object detection thread, which employs an enhanced Yolov5s to extract semantic information in every input frame, enabling the filtering of dynamic features for initial tracking and localization. Additionally, we integrate multi-view geometry to further discriminate dynamic feature information, thereby increasing the precision and robustness of localization systems. Finally, experiments were executed on the TUM RGB-D dataset to prove the performance of the proposed algorithm. The results demonstrate strong performance on most datasets, showing a 97.06% improvement in localization accuracy over the original ORB-SLAM3 algorithm in indoor dynamic environments.  
dynamic environment; semantic information; SLAM; ORB-SLAM3 +
Footnote †: journal: Applied Sciences  
0000-0001-5000-0000-0000-0000-0000-0000-0000-0000-0000-0000-0000-0000-0000-0000-0000-00000-0000-00000-00000-0000-00000-00000-0000  
Recent studies have introduced various enhancements for such scenarios. For instance, ref. [6] proposes refining camera localization by filtering potential dynamic objects and constructing a static map, though excessive feature removal may destabilize SLAM tracking. Meanwhile, refs. [7; 8; 9] integrate semantic segmentation or scene flow to improve dynamic object detection. While these techniques boost perceptual precision, they also escalate computational demands.  
To overcome these limitations, we propose DI-SLAM, a dynamic SLAM algorithm that extends ORB-SLAM3 through semantic-geometric collaboration. Our approach reliably distinguishes between static and dynamic features, ensuring that subsequent pose estimation is based solely on reliable static features, thereby improving the system's localization accuracy and robustness in dynamic environments. The principal contributions of this research are summarized below:  
1. We propose a visual SLAM system designed for indoor dynamic environments, significantly improving pose estimation accuracy and demonstrating enhanced robustness in dynamic indoor scenes.
2. We develop a dynamic feature filtering approach leveraging Yolov5s detection and multi-view geometry. This method effectively addresses the issues of false positives and false negatives in dynamic feature recognition through a dual-validation mechanism.
3. We evaluated our algorithm's performance on the TUM RGB-D dataset. The results show that DI-SLAM effectively filters moving features and improves localization accuracy in highly dynamic scenarios.  
The subsequent sections are structured accordingly: Section 2 provides the related work of dynamic SLAM approaches. The system framework and dynamic filtering strategy is detailed in the Section 3, highlighting the improved Yolov5s model and multi-view geometry method. Section 4 discusses the evaluation methodology and analyzes the experimental results of DI-SLAM. Finally, Section 5 wraps up the paper.