# Salient Semantic Segmentation Based on RGB-D Camera for Robot Semantic Mapping
## 1 Introduction  
The semantic map refers to the application of neural network-based object recognition, image segmentation, and other computer vision technology to the camera-based geometric map [1; 2; 3; 4], which has developed rapidly with the Convolutional Neural Network (CNN) applied in computer vision. However, CNN does not sufficiently solve the challenge of image pixel-level segmentation. On this basis, [PERSON] et al. [5] proposed a Fully Convolutional Network (FCN), which abandoned the fully connected layer commonly used in CNN and realizes pixel-level semantic segmentation. FCN is the pioneer of semantic segmentation. Since then, outstanding network architectures such as U-Net, SegNet, PSPNet, DeepLab, and CANet et al. [6; 7; 8] have been proposed one after another. It is still a big challenge for them to realize accurate image semantic segmentation in real-time of all the geometric obstacles under the limited computing power condition of the mobile robot's onboard processor, such as the speed (FPS) of PSPNet is 5 [6], and the speed (FPS) of DeepLab-v2 is 6 [7]. A semantic map helps robots classify obstacles according to semantic information and can help mobile robots understand the geometric environment further than the traditional robot map that only has geometric information. Which can be widely used in the field of autonomous cars [9], intelligent manufacturing [10], UAVs [11; 12], service robots [13], etc. An ideal SLAM (Simultaneous localization and mapping) should quickly relocate when encountering a robot kidnapping problem or using a map again. Semantic mapping can give semantic labels to the various objects in a geometric environment. This canhelp robots improve the accuracy of repositioning and further mapping, plan the motion more intelligently in the process of autonomous movement, and have better interaction capabilities with the environment compared with geometric maps [14; 15; 16]. Salient objects are the most eye-catching objects in the robot's view field. They are different from the environment in shape, color, direction, depth information, etc., usually as the preprocessing stage of advanced computer vision tasks [17; 18; 19]. Such as, we proposed to realize the fusion of two kinds of sensor information based on salient objects in previous work [20]. We proposed to estimate the relative position and attitude based on salient objects in RGB-D cameras [21]. We further worked on the real-time semantic mapping of salient objects in RGB-D cameras in this study. Salient objects are mobile robots' most prominent and important obstacles in map building. To reduce the calculation amount during semantic segmentation, we sought to achieve semantic segmentation on salient objects in the mapping process because semantic segmentation is a much more complicated multi-classification problem than salient object detection. It is the deeper scene understanding. The more obstacles that need semantic segmentation, the greater the mobile robots' computational pressure owing to the details of the picture that are considered more carefully. It has more training sets that need pixel-level annotation and be used for the training model. However, semantic segmentation still faces the bottleneck of lacking excellent manual annotation datasets. We propose the salient object semantic segmentation under limited computing power. This paper attempts to replace semantic segmentation's huge calculation amount with the calculation of salient object detection and recognition. Semantic segmentation faces bottlenecks limited by the framework structure and dataset. Semantic segmentation accuracy is upper bound limited by the consistency of annotation and pixel accuracy of artificial semantic segmentation annotation that is difficult to guarantee [22]. The neural network training time is consuming, and the shortcomings of a large amount of segmentation calculation make it not easy to implement in the industry communities and not as reliable as salient object detection and object recognition.