LiDAR-Visual SLAM with Integrated Semantic and Texture Information for Enhanced Ecological Monitoring Vehicle Localization

Ecological monitoring vehicles are equipped with a range of sensors and monitoring devices designed to gather data on ecological and environmental factors. These vehicles are crucial in various fields, including environmental science research, ecological and environmental monitoring projects, disaster response, and emergency management. A key method employed in these vehicles for achieving high-precision positioning is LiDAR (lightlaser detection and ranging)-Visual Simultaneous Localization and Mapping (SLAM). However, maintaining highprecision localization in complex scenarios, such as degraded environments or when dynamic objects are present, remains a significant challenge. To address this issue, we integrate both semantic and texture information from LiDAR and cameras to enhance the robustness and efficiency of data registration. Specifically, semantic information simplifies the modeling of scene elements, reducing the reliance on dense point clouds, which can be less efficient. Meanwhile, visual texture information complements LiDAR-Visual localization by providing additional contextual details. By incorporating semantic and texture details from paired images and point clouds, we significantly improve the quality of data association, thereby increasing the success rate of localization. This approach not only enhances the operational capabilities of ecological monitoring vehicles in complex environments but also contributes to improving the overall efficiency and effectiveness of ecological monitoring and environmental protection efforts.