# Salient Semantic Segmentation Based on RGB-D Camera for Robot Semantic Mapping
## 5 Conclusions  
We propose a method for salient semantic segmentation in the mapping process based on the mobile robot RGB-D camera. The primary motivation behind salient semantic segmentation is that the semantic segmentation of key objects can reduce the calculation amount and bypass the bottleneck problems that conventional semantic segmentation faces.  
We tested the trained neural network YOLO V3 on the various datasets, as shown in Table 5. The main YOLO V3 performance evaluation and test were carried out with the objects data persons, dogs, and cars. The traffic scene BDD 100K containing complex traffic scenes and RGB-D people containing indoor pedestrian scenes, respectively, were used to verify the usability of the proposed method. We found that the good performance of salient object recognition depended on the salient object detection's imaging effect. Further, the salient object detection's imaging effect affected the salient object contour's segmentation quality and the point cloud's segmentation effect. If the salient object detection's imaging effect is too bad will limit the proposed method. Finally, the TUM dataset was used to verify the feasibility of our method for generating point cloud images. This is based on using salient objects to replace semantic segmentation of all the obstacles, which can save the calculation amount and bypass the semantic segmentation bottleneck problems, providing a technical reference for real-time semantic segmentation algorithm in mobile robot mapping. The comparative experiments show that our method is easier to train and effectively segment salient objects with semantic information and can further transform them into semantic landmarks in the 3D point cloud map. The landmarks can provide relative position references for robot repositioning, further mapping, path planning, and improving human-computer interaction.  
Conceptualization, L.H. and Y.Z.; methodology, L.H.; software, H.Y. and L.H.; validation, L.H., Y.W. and Y.Z.; formal analysis, Y.W.; investigation, L.H. and S.T.; resources, S.T.; data curation, H.Y.; writing--original draft preparation, L.H.; writing--review and editing, L.H.; visualization, Y.W.; supervision, Y.Z. and L.H.; project administration, Y.Z.; funding acquisition, Y.Z. All authors have read and agreed to the published version of the manuscript.  
Figure 10: Point cloud images (**a**,**b**) are generated from RGB image (**d**) and segmented RGB image (**e**) combined with depth map (**c**), respectively.  
**Funding:** This research is supported in part by the Research Project of China Disabled Persons Federation on assistive technology under Grant 2022 CDPFAT-01, in part by the Science and Technology Planning Project of Chongqing Changshou District under Grant cskj2022014, in part by the National Nature Science Foundation of China under Grant 51775076, 51905065.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data used to support the findings of this study are available from the corresponding author upon request.  
**Conflicts of Interest:** The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.