# Salient Semantic Segmentation Based on RGB-D Camera for Robot Semantic Mapping
## 2 Related Work  
In mobile robot semantic mapping, much related research has been carried out in the semantic segmentation field. Here, we mainly focus on the current representative methods of the features used for object recognition, the reduction of semantic segmentation calculation, and the real-time semantic mapping that is most relevant to our work. [PERSON] et al. [40] proposed a method of color segmentation using a simplified PCNN to recognize objects. [PERSON] et al. [41] proposed a food recognition method combining color and texture features. Their method could adaptively learn representative colors or textures for food recognition. Relevant works showed that color features do not depend on the image's direction, angle, and size, which could be used for object recognition. Still, if the different objects' colors are relatively similar and there is no obvious texture difference, it was unsuitable. This problem could be solved by using a more stable shape feature that is rich in semantic information [27; 28; 42]. RGB images have higher data dimensions and richer feature information than grayscale images used for object recognition, which has been the subject of much research work. [PERSON] et al. [43] proposed a method to recognize outdoor scene objects using local color, texture, and context features. [PERSON] et al. [44] proposed a method of fruit recognition used for supermarkets, which mainly includes the fruit's shape and texture features. Object recognition based on multiple features in RGB images is the mainstream method at present, which can make up for the shortcomings of each feature. The more features and details are considered for neural networks, the more computation is needed. To better meet the construction of semantic maps for mobile robots with limited computing power that requires high real-time performance, we propose recognizing objects based on the contour shapes because they are relatively stable and rich in semantic information. Furthermore, we use the grayscale image to show the contour shape of the objects because most details of contour shapes can be easily presented in grayscale images. [PERSON] et al. [45] proposed a method using a dense segmentation network (DS-Net) to achieve good performance of semantic mapping fusion. [PERSON] et al. [46] proposed a reconstruction method for a large outdoor 3D dense semantic map based on monocular vision. The algorithm could reconstruct globally consistent semantic maps in large-scale outdoor scenes. In their article, if the scene became larger and more types of semantic segmentation were considered, there was more consideration for the image details. The calculation amount was larger when the mobile service robot faced an environment full of objects, such as semantic segmentation of various daily necessities, furniture, office supplies, and other items in the indoor mapping scene. Ensuring the multiple objects' accurate segmentation and the semantic mapping's real-time requirements is not a small challenge. [PERSON], [PERSON] et al. [47] proposed a method of semantic segmentation that reduced the calculation amount by 25 times without significantly impacting the quality of the results. [PERSON] et al. [48] proposed a deep architecture that could run in real-time while providing accurate semantic segmentation. The core of their deep architecture was a new layer that uses residual connections and factored convolutions to maintain efficiency while maintaining excellent performance. [PERSON] et al. [49] and [PERSON] et al. [50] also did a lot of work on real-time semantic segmentation. The above works mainly focused on the neural network structure. The speed of most semantic segmentation methods will increase at the cost of mAP reduction. We propose to explore the semantic segmentation of salient objects from the data source to reduce the calculation of semantic mapping for all objects, and while maintaining a high FPS by using salient object detection to filter out the background objects that are not important but consume computing resources.