# Navigation Map Construction Based on Semantic Segmentation and Multi-Submap Integration
## 2 Related Works  
### Visual SLAM  
The goal of the SLAM process is to map an unknown environment using signals from sensors while simultaneously localizing the sensor within that environment. Feature-based vSLAM methods commonly use feature detection algorithms, such as scale-invariant feature transform (SIFT) [16], speeded-up robust features (SURF) [17], and ORB [18], to extract key points from images for matching. ORB-SLAM [3] has become one of the most widely adopted visual SLAM solutions. It innovatively integrates three threads: tracking, local mapping, and loop detection. The loop detection thread uses the bag-of-words model (DBoW) [19] to detect loop closures based on image similarity, achieving notable performance in both processing speed and map construction accuracy. ORB-SLAM2 [4] extends the framework to support stereo and RGB-D cameras, improving scale consistency and robustness to dynamic environments. The system effectively integrates pose graph optimization and keyframe-based mapping, enhancing its performance in real-world applications. The latest version, ORB-SLAM3 [5], unifies monocular, stereo, RGB-D, and visual-inertial SLAM into a single framework, making it one of the most versatile and widely used SLAM systems today. However, due to inherent randomness during SLAM initialization, the coordinate systems generated by the system are often arbitrary, leading to inconsistencies in map coordinates across different runs. This randomness complicates the maintenance of global consistency between submaps, especially in long-duration systems and when merging maps across multiple scenes. In SLAM systems, both camera poses and environmental features are estimated within relative coordinate systems, meaning that each system restart may result in a coordinate system that differs significantly from the previous one. Consequently, merging or stitching submaps from multiple runs requires complex coordinate transformations and alignment procedures.  
Several vSLAM systems integrate GNSS data to obtain absolute coordinate values. In GNSS-Visual-Inertial Navigation System (GVINS) [20], raw GNSS measurements are tightly integrated into the system, enabling global positioning in both indoor and outdoor environments. The method proposed in GVINS is based on Visual-Inertial Navigation System-Monocular (VINS-Mono) [21], but it does not incorporate improvements to the visual processing. As a result, the robustness and accuracy of GVINS may degrade in environments with poor GNSS signals. Reference [22] presented a vehicle dynamics and intermittent GNSS-assisted visual-inertial odometry state estimator to address the localization problem in autonomous vehicles. In Reference [23], an optimization-based visual-inertial SLAM algorithm is proposed that tightly couples raw GNSS measurements. By jointly minimizing reprojection errors and GNSS measurement errors within a sliding window, the system improves robustness in challenging environments. In Reference [24], a semi-tightly coupled sensor fusion system based on factor graph optimization was proposed. By optimizing the back end with factor graphs and decoding GNSS data to reduce cumulative errors, the system enhances positioning accuracy and reliability. Compared with the aforementioned studies that enhance positioning accuracy through extensive GNSS integration, our work, while not integrating with GNSS as deeply, addresses the issue of random origin placement in the local coordinate systems of visual navigation systems lacking global positioning constraints. This outcome holds a certain engineering significance for map reuse. Moreover, the aforementioned studies did not consider how to utilize the constructed map for navigation, whereas our work constructs a global grid map that provides input data directly parsable by path-planning algorithms.