# DFD-SLAM: Visual SLAM with Deep Features in Dynamic Environment
## 5 Conclusions  
In this paper, we present a dynamic SLAM system based on deep features, named DFD-SLAM. The system utilizes HFNet for feature extraction combined with semantic in \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**TUM-RGBD (ATE)**} \\ \cline{2-7}
**Sequence** & **DFD (H)** & **DFD (HS)** & \multicolumn{2}{c}{**DFD (HSD)**} & \multicolumn{2}{c}{**DFD (BSD)**} \\ \cline{2-7}  & **ATE** & **ATE** & **Boost** & **ATE** & **Boost** & **ATE** & **Boost** \\ \hline W/half & 0.162 & 0.024 & **0.852** & 0.026 & 0.840 & 0.028 & 0.827 \\ W/rpy & 0.117 & 0.032 & 0.726 & 0.029 & **0.752** & 0.035 & 0.701 \\ W/static & 0.021 & 0.006 & 0.714 & 0.005 & **0.762** & 0.008 & 0.619 \\ W/xyz & 0.077 & 0.009 & 0.883 & 0.007 & **0.909** & 0.011 & 0.857 \\ average & 0.094 & 0.018 & 0.794 & 0.017 & **0.816** & 0.021 & 0.751 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Ablation experimental result of absolute trajectory error in TUM-RGBD. Figure 8: Dynamic point culling flowchart in \(W/rpy\) sequence. Each of these lines represents a complete culling process. Each column represents a cull step. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Systems** & **Tracking Cost (ms)** & **Hardware** \\ \hline ORB-SLAM3 & 18.92 & Intel12700h (Intel) \\ CDS-SLAM & 37.96 & Ryzen7-5800H RTX3070 (AMD Santa Clara, CA, USA) (Nvidia) \\ DynaSLAM & 195.00 & Nvidia Tesla M40 GPU (Nvidia) \\ PR-SLAM & 50–60 & RS-3600 RTX3070 (AMD) (Nvidia) \\ Ours & 47.83 & Intel12700h GTX1070 TI (Intel) (Nvidia) \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Computational cost. formation from YOLOv8, addressing the shortcomings of related works. This combination ensures high precision and robust performance across various environments. We introduce a frame rotation estimation strategy to allow the system to select different descriptors for appropriate scenes. Additionally, we developed a more accurate strategy for dynamic feature point elimination. Experimental results demonstrate that our deep features integrated with frame rotation estimation outperform traditional manual extraction and pure deep feature extraction in terms of tracking accuracy in static environments. In dynamic environments, our dynamic feature point elimination strategy is more precise in excluding dynamic feature points, without over- or under-eliminating them. These advancements enable our system to achieve the highest accuracy among similar outstanding works. Moreover, we accelerated inference with TensorRT, ensuring that this dual-model system maintains good real-time performance with GPU acceleration. We believe that there is still some method to improve the real-time performance. Future work could consider integrating semantic information from keyframes, similar to PR-SLAM, combined with dynamic probability for dynamic point elimination. This approach could significantly reduce computational time and load, potentially enabling operation on less powerful mobile platforms. Conceptualization, W.Q. and J.P.; methodology, W.Q., J.P. and H.Z. ; software, W.Q. ; validation, W.Q., J.P. and H.Z. ; formal analysis, W.Q. ; investigation, J.P.; resources, J.P.; data curation, W.Q. ; writing--original draft preparation, W.Q. and H.Z. ; writing--review and editing, W.Q., J.P. and H.Z. ; visualization, W.Q. ; supervision, J.P.; project administration, J.P. All authors have read and agreed to the published version of the manuscript. The authors are highly thankful to the National Natural Science Foundation of China (No. 62063006), the Natural Science Foundation of Guangxi Province (No. 2023 GXNSFAA026025), the Innovation Fund of Chinese Universities Industry-University-Research (ID: 2023 RY018), to the Research Project for Young and Middle-Aged Teachers in Guangxi Universities (ID: 2020 KY15013), and the Special Research Project of Hechi University (ID: 2021 GCC028). Not applicable. The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.