# Navigation Map Construction Based on Semantic Segmentation and Multi-Submap Integration
## 1 Introduction  
Visual simultaneous localization and mapping (SLAM) methods, such as the oriented FAST [1] and rotated BRIEF [2] (ORB) SLAM series [3, 4, 5], can achieve decimeter-level positioning accuracy in feature-rich environments [6]. However, since most of these systems are feature-based, they extract visual features (e.g., points, lines, and corners) as landmarks and generate sparse or semi-dense point cloud maps for localization. These maps are unsuitable for navigation and path planning as they do not explicitly delineate traversable areas in space. Navigation map construction using dense depth data from red green blue depth (RGB-D) cameras is a well-established approach [7, 8, 9]. Monocular and stereo cameras cannot directly acquire depth information, necessitating the use of multi-view geometric algorithms or depth estimation convolutional neural networks (CNNs) to achieve comparable results [10]. However, dense depth estimation is computationally expensive and prone to errors, particularly in textureless regions such as road surfaces. Furthermore, the position and orientation generated by classical SLAM methods are defined within a local coordinate system, where the origin is determined by the camera's initial position. Invisual navigation systems without the constraint of global positioning information, running the algorithm in different locations leads to localization and mapping results in distinct coordinate systems, complicating the integration of these measurements. To address these limitations, we propose a navigation map construction method that does not rely on dense depth estimation, combining Panoptic Fully Convolutional Network (FCN) [11] and visual SLAM to provide global positioning information in the form of an occupancy grid, and the map provides directly parsable input data for path-planning algorithms. PanopticFCN leverages global context through fully convolutional processing rather than relying on local textures or gradients, and thus, it outperforms the depth estimation network in low-texture regions [12,13]. The fully convolutional architecture of PanopticFCN enables it to efficiently extract features at multiple scales, dynamically generating kernels to encode both fine-grained details and global contextual information. Moreover, the dense depth estimation requires depth computation for each pixel in the image [14], while our method fits the plane by sparse points, and the number of data is significantly reduced. As a result, our method is more robust in low-texture regions such as the ground, while having a lower computational cost compared to methods that utilize dense depth estimation for mapping. Our algorithm first applies semantic segmentation to identify semantic ground, which is then gridded into a two-dimensional (2D) occupancy grid map based on the semantic information of each grid cell. This map employs global navigation satellite systems (GNSS) to assign both occupancy information and global coordinates to each cell, thereby reflecting the occupancy state of the corresponding real-world locations. To balance map sparsity with computational efficiency, the cell size in the occupancy grid map (OGM) [15] is set to 0.1 \(\times\) 0.1 m. We represent relatively large environments using multiple submaps, which can be dynamically loaded and stored. During map updates, only the submaps surrounding the vehicle are accessed, ensuring real-time map updates and significantly reducing computational and storage demands. In addition, we show an example application that demonstrates that the maps obtained by our method can be practically used in AGV navigation tasks. The main contributions can be summarized as follows:  
1. The method does not rely on dense depth estimation, and reconstructs only the road surface and obstacles, omitting complex three-dimensional (3D) environmental reconstruction, and generates a lightweight 2D grid map, which can provide a directly parsable input for path-planning algorithms. 2.