## 5 Conclusions  
In this paper, we propose a method to utilize the symmetry of human-made objects to enhance landmark estimation for the monocular object-SLAM system. Based on our light-weight single-frame symmetry-detection technique, we add symmetry factors in the back-end graph of object-SLAM, which significantly improves the object-mapping accuracy according to experiments. The symmetry constraints on the object orientation provide more information for semantic navigation and help the estimation of the scale and center of the objects. Our proposed model lacks competitiveness with other task-specific SLAM models for the specific task, such as the cube-SLAM on ideal cubic objects. Moreover, the proposed method is based on a general geometric model, which is purely model-driven and lacks scalability to data with a more complicated structure or noisy data. Therefore, the combination of the current SLAM model with data-driven machine learning methods may improve the performance of SLAM and substantially reduce the mapping error.  
Considering future work, it will be promising to further explore the properties of objects to help robots to better, or even actively, build an object-level map.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Datasets**} & \multicolumn{2}{c}{**Quadric-SVD**} & \multicolumn{2}{c}{**Quadric-SLAM**} & \multicolumn{2}{c}{**Cube-SLAM**} & \multicolumn{2}{c}{**Ours**} \\  & **T** & **R** & **S** & **T** & **R** & **S** & **S** & **T** & **R** & **S** \\ \hline ICL\_home & 0.85 & 39.15 & 0.10 & 0.29 & 37.14 & 0.30 & 0.49 & **0.21** & **6.14** & **0.65** \\ Fr1\_desk & 0.40 & 46.99 & 0.31 & 0.33 & 47.39 & 0.40 & - & **0.08** & **12.21** & **0.45** \\ Fr2\_desk & 0.34 & 36.70 & 0.25 & 0.24 & 38.13 & 0.42 & - & **0.10** & **11.85** & **0.48** \\ Fr3\_cabinet & 0.06 & 19.98 & 0.34 & 0.05 & 18.31 & 0.33 & **0.64** & **0.05** & **9.87** & 0.43 \\ Real\_robot & 1.58 & 29.85 & 0.24 & 1.14 & 25.51 & 0.19 & - & **0.31** & **24.60** & **0.35** \\ \hline Average & 0.65 & 34.53 & 0.25 & 0.41 & 33.30 & 0.33 & - & **0.15** & **12.93** & **0.47** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Object-mapping results. The translation, rotation and shape error are calculated and denoted as T, R, S.  
**Author Contributions:** The experimental model was built by [PERSON] Analyses were carried out by [PERSON] and [PERSON]. The organization of data was led by [PERSON] The descriptions of text use were led by [PERSON] All authors have read and agreed to the published version of the manuscript.  
**Funding:** Thiswork was supported, in part, by the National Key Research and Development Program of China under Grant 2020 YFB1313600, and, in part, by the National Key Research and Development Program of China under Grant 2022 YFB4400402.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The datasets were derived from TUM RGB-D ([[https://vision.in.tum](https://vision.in.tum)]([https://vision.in.tum](https://vision.in.tum)).  
de/data/datasets/rgbd-dataset (accessed on 1 January 2022)) and ICL-NUIM ([[https://www.doc.ic](https://www.doc.ic)]([https://www.doc.ic](https://www.doc.ic)).  
ac.uk/~ahanda/VaFRIC/icluuim.html (accessed on 1 January 2022)).  
**Acknowledgments:** The authors thank [PERSON] for his assistance.  
**Conflicts of Interest:** The authors declare no conflict of interest.