Navigation Map Construction Based on Semantic Segmentation and Multi-Submap Integration

Traditional visual simultaneous localization and mapping (SLAM) systems typically generate sparse or semi-dense point cloud maps, which are insufficient for effective navigation and path planning. Constructing navigation maps through dense depth estimation generally entails high computational costs, and depth estimation is prone to errors in weakly textured regions such as road surfaces. Furthermore, traditional visual SLAM methods rely on local relative coordinate systems, making it extremely challenging to merge mapping results from different coordinate frames in navigation systems lacking global positioning constraints. To address these limitations, this paper presents a multi-submap fusion mapping method based on semantic ground fitting and incorporates global navigation satellite system (GNSS) to provide global positioning information via occupancy grid maps. The method emphasizes the integration of low-cost sensors into a unified system, aiming to create an accurate and real-time mapping solution that is cost-effective and highly applicable. Simultaneously, a multi-submap management mechanism is introduced to dynamically store and load maps, updating only the submaps surrounding the vehicle. This ensures real-time map updates while minimizing computational and storage resource consumption. Extensive testing of the proposed method in real-world scenarios, using a self-built experimental platform, demonstrates that the generated grid map meets the accuracy requirements for navigation tasks.