## 5 Experimental Design and Results Analysis
### Simulated Mining Environment Experiment
Secondly, the enhanced feature matching method in the motion estimation process markedly decreases mismatching rates, ensuring steady tracking in regions with significant terrain variations. The algorithm effectively consolidates disparate local observations into a coherent environmental representation in the mapping step. The results of the dense reconstruction precisely represent the spatial configuration of the mining environment, encompassing critical details such as terrain variations and rock distribution, and are devoid of noticeable point cloud distortion or overlap issues. Furthermore, the experimental findings confirm the algorithm's resilience in managing extensive contexts. The method reliably upholds constant localization precision within the testing zone without substantial cumulative errors. In regions susceptible to tracking failures, such as turns, the algorithm effectively sustains continuous system functioning using proficient feature management and posture optimization techniques. The experimental findings unequivocally illustrate the resilience and precision of the proposed method in intricate open-pit mining situations, establishing a strong basis for its practical application deployment. The experiment in the simulated open-pit mining environment utilized a standard mining road as the test scenario, with site dimensions measuring 20 m long and 4.4 m wide. The experimental platform traversed the mining operation route at a steady velocity of 0.6 m/s, employing the SLAM technique introduced in this research to generate real  
Figure 21: Real scene test results: (**left**) three-dimensional trajectory reconstruction; (**right**) feature detection and dense mapping. time three-dimensional dense maps. The testing findings illustrate the produced three-dimensional point cloud map, as depicted in Figure 22. The efficacy of the algorithm presented in this study for dense mapping can be effectively validated by juxtaposing the generated dense point cloud map with actual photos of the mining site. The photos demonstrate that the dense map produced by this algorithm effectively reconstructs the spatial distribution of rocks, mining tunnels, and other natural terrain features in the open-pit mining environment. In portraying environmental details, the point cloud map exhibits no discernible overlap, distortion, or deformation and precisely reconstructs elements such as terrain undulations. The geometric characteristics closely align with the landscape, suggesting the system exhibits robust three-dimensional mapping skills in mining contexts. Upon further analysis of the algorithm's performance, the proposed SLAM algorithm demonstrates substantial decreases in both absolute and relative trajectory errors compared to the ORB-SLAM3. This signifies that the method enhances localization precision, particularly in intricate environments, such as open-pit mines, facilitating more efficient long-term stable localization and map development. Moreover, incorporating line feature extraction and matching techniques significantly improves mapping precision in dynamic mining contexts. In simulated open-pit mining scenarios, the algorithm reliably produces high-quality three-dimensional dense point cloud maps, even amidst intricate terrain and environmental variations, offering dependable technical support and a foundation for the navigation and localization of mining operation robots in complex settings.