# Navigation Map Construction Based on Semantic Segmentation and Multi-Submap Integration
## 2 Related Works
### Vision-Based Navigation Map Construction  
In recent years, many researchers have used RGB-D cameras for dense depth reconstruction to generate navigational grid maps, representing scene geometry through high-density 3D point clouds [7; 8; 9]. RGB-D cameras, based on the principle of active optical ranging, achieve real-time acquisition of pixel-level depth perception data by directly measuring the phase or time delay of signals reflected from object surfaces. These hardware characteristics enable RGB-D visual SLAM systems to establish dense 3D environments through direct physical measurements without relying on complex stereo matching algorithms. KinectFusion [7] is a milestone work and serves as a representative example. It achieves pose estimation and dense point cloud registration through the Iterative Closest Point (ICP) algorithm and introduces the Truncated Signed Distance Function (TSDF) for global optimization and incremental fusion of scene surfaces. This work validated the feasibility of RGB-D cameras in high-precision environment modeling and provided a significant reference for subsequent vision-based SLAM and navigational map construction. SemanticFusion [8] introduces semantic understanding into SLAM by fusing pixel-wise semantic segmentation from deep learning models with spatial mapping. By accumulating and refining semantic labels over time, it enhances scene comprehension and improves robustness in complex environments. MaskFusion [9] further extends this framework by incorporating instance-level segmentation, allowing the system to distinguish different elements in the environment. Using deep-learning-based instance masks, it refines the classification and representation of various regions, improving contextual awareness. Reference [25] proposed an improved real-time indoor localization system based on ORB RGB-D SLAM. While generating an ORB-SLAM sparse feature map, the system utilizes camera poses estimated by visual SLAM and laser scan data extracted from point clouds to construct and continuously update a 2D grid map in real time.  
Dense depth estimation using stereo cameras can also be used to mapping, and in recent years, a number of stereo matching algorithms have appeared to obtain depth maps of entire images. Reference [26] employed a variational autoencoder (VAE) to improve stereo matching by learning a probabilistic disparity distribution. However, this method is computationally expensive in both training and inference and struggles with disparity estimation in low-texture and occluded regions. Reference [27] utilized cascaded residual learning to refine disparity estimation by incorporating contextual information. While it performs well in recovering fine details, its high computational complexity limits its scalability, particularly in large-scale scenes and under extreme lighting conditions. Reference [28] enhanced stereo matching by using an iterative geometry encoding volume (IGEV) to refine disparity estimation. While it improves accuracy, it remains computationally intensive and slow, limiting real-time applicability. Like other deep learning methods, it struggles with low-texture regions and occlusions. Reference [29] employed a real-time stereo matching network using Spatial Attention-Guided Upsampling to refine cost volumes and disparity maps, preserving high-frequency details and depth discontinuities. While it improves accuracy and efficiency, it may struggle with occlusions and low-texture regions, and its attention mechanisms could lead to higher memory consumption and potential artifacts in complex scenes. Unlike the above methods, our method does not calculate the depth value of each pixel in the whole image when mapping, while fitting the plane by sparse points, and the number of data is significantly reduced. Furthermore, our approach can construct maps in real time in real environments.