# DFD-SLAM: Visual SLAM with Deep Features in Dynamic Environment  
[PERSON]  
1 College of Automation, Guangqi University of Science and Technology, Liuzhou 545000, China; [EMAIL_ADDRESS] (W.Q.); [EMAIL_ADDRESS] (H.Z.)1 College of Automation, Guangqi University of Science and Technology, Liuzhou 545000, China; [EMAIL_ADDRESS] (W.Q.); [EMAIL_ADDRESS] (H.Z.)1 College of Automation, Guangqi University of Science and Technology, Liuzhou 545000, China; [EMAIL_ADDRESS] (W.Q.); [EMAIL_ADDRESS] (H.Z.)1 College of Automation, Guangqi University of Science and Technology, Liuzhou 545000, China; [EMAIL_ADDRESS] (W.Q.); [EMAIL_ADDRESS] (H.Z.)1 College of Automation, Guangqi University of Science and Technology, Liuzhou 545000, China; [EMAIL_ADDRESS] (W.Q.); [EMAIL_ADDRESS] (H.Z.)1 College of Automation, Guangqi University of Science and Technology, Liuzhou 545000, China; [EMAIL_ADDRESS] (W.Q.); [EMAIL_ADDRESS] (H.Z.)1 College of Automation, Guangqi University of Science and Technology, Liuzhou 545000, China; [EMAIL_ADDRESS] (H.Z.)1 College of Automation, Guangqi University of Science and Technology, Liuzhou 545000, China; [EMAIL_ADDRESS] (W.Q.); [EMAIL_ADDRESS] (H.Z.)1 College of Automation, Guangqi University of Science and Technology, Liuzhou 545000, China; [EMAIL_ADDRESS].  
enhancing system robustness. However, these solutions still have problems. In HFNet-SLAM [7], issues with poor rotation robustness in CNN-based feature extraction networks are mentioned. Moreover, in Crowd-SLAM, the authors found that removing too many feature points can reduce accuracy [8]. This problem was also confirmed by the authors of CDS-SLAM [9].  
To tackle these problems, this paper proposes a precise and robust indoor SLAM system, supporting monocular, monocular-inertial, and RGB-D inputs. By leveraging TensorRT for accelerated model inference [10], the system integrates HFNet for feature extraction and utilizes YOLOv8 for semantic information, harnessing the advantages of deep features to adapt to dynamic environments [11; 12]. The design of the system circumvents issues of deep feature loss under rotation, effectively combining manual and deep learning descriptors. The specific contributions of this paper are as follows:  
1. A real-time dynamic SLAM system based on the ORB-SLAM3 framework is proposed, supporting multiple sensor input modalities. Utilizing HFNet for both local and global feature extraction significantly enhances the tracking and loop closure performance of ORB-SLAM3. Simultaneously, YOLOv8 contributes to the precise removal of feature points, leveraging semantic information. The system maximizes the benefits of deep features extraction in dynamic SLAM settings.
2. A frame rotation estimation method is introduced, where geometric consistency detection calculates possible rotation centers based on optic flow vectors to determine the appropriate usage of descriptors generated by HFNet or re-extraction of ORB descriptors under different circumstances. The system effectively combines the advantages of deep features and traditional manual methods.
3. A better feature point removal strategy is proposed, integrating geometric consistency detection to accurately filter semantic information from YOLOv8 and ensure precise removal of dynamic feature points, avoiding over-removal scenarios.  
The paper is structured as follows: Section 2 introduces related work and discusses the differences in our approach. Section 3 provides a detailed overview of the system's construction and the specific algorithmic processes. Section 4.1 introduces the experimental design and evaluation metrics. Section 4.2 details the performance of DFD-SLAM on the TUM-VI dataset, analyzing its tracking and loop closure capabilities. Section 4.3 thoroughly tests the performance of DFD-SLAM on the TUM-RGBD dataset and examines its removal strategy in detail. In Section 4.4, we compare the real-time performance of DFD-SLAM with other outstanding dynamic SLAM systems. Section 5 delves into discussions and future prospects for the system.