## 5 Experimental Design and Results Analysis
### Simulated Mining Environment Experiment  
To validate the proposed system's applicability in real-world scenarios and to replicate actual application environments, the experiment utilized a construction site that emulates a \begin{table}
\begin{tabular}{c c c} \hline \hline
**Sequence** & **Point Cloud Map Size/MB** & **Octree Map Size/MB** \\ \hline fr1\_desk & 6.5 & 0.0435 \\ fr1\_xyz & 7.3 & 0.0457 \\ fr2\_xyz & 6.2 & 0.0524 \\ fr2\_desk & 29.8 & 0.2032 \\ fr3\_long\_office & 53.2 & 0.3146 \\ fr3\_walk\_rpy & 5.5 & 0.0368 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of storage space between point cloud map and octree map. genuine open-pit mining construction setting, as depicted in Figure 19b. A mining mapping robot prototype, illustrated in Figure 19a, was employed for field testing. This experiment aimed to validate the dense point cloud map and octree map construction capabilities of the enhanced algorithm in real-world settings. The experimental design chose a standard open-pit mining construction road section for testing, which encompasses typical open-pit mining environmental factors, including gravel rock walls and uneven terrain, to assess the algorithm's dense mapping accuracy and mapping expression capability in complex environments. The experimental hardware platform comprised an Intel Core i7-10510U processor (4 cores), 8 GB of RAM, and an Intel RealSense Depth Camera D435i for data acquisition. The system operated within the Ubuntu 20.04 environment, utilizing an algorithm implemented on the ROS Noetic version. This research selected a representative stretch of a mining machinery operation route to assess the dense mapping performance of the proposed algorithm in open-pit mining situations. Figure 20 illustrates that the test scenario encompasses characteristic aspects of a mining area, including uneven terrain variations and gravel deposits. To guarantee the trustworthiness of experimental results, the test platform traversed the designated path ABC at a constant velocity of 0.6 m/s, as illustrated in Figure 20b. This path design incorporates linear and curved segments, efficiently assessing the algorithm's efficacy under varying motion situations. Figure 19: Simulated open-pit mining experimental platform and environment: (**a**) mapping robot prototype; (**b**) simulated open-pit mining testing environment: 1—portable computer, 2—Intel RealSense D435i depth camera, 3—auxiliary display screen, and 4—mobile chassis. Figure 20: Simulated bumpy road mining environment: (**a**) actual scene; (**b**) movement trajectory. Figure 21 presents the comprehensive experimental outcomes for this test scenario. The left picture illustrates the three-dimensional trajectory reconstruction results, with coordinate axes indicating a maximum elevation variation of around 40 m and a lateral displacement range of about 50 m. The rebuilt trajectory distinctly illustrates the platform's travel pattern in intricate terrain. Trajectory analysis demonstrates that, despite substantial height variations, the algorithm ensures continuous and stable pose estimation, with reconstructed trajectories closely aligning with actual path elevations. The right image in Figure 21 illustrates the outcomes of feature extraction, with green points and lines denoting successfully extracted and matched feature points and lines. Notwithstanding inconsistent illumination and disordered rock distribution in the surroundings, the method reliably recovers adequate valid feature points, yielding dependable observational data for future posture estimation. A subsequent examination of the experimental procedure reveals considerable benefits of this method in addressing difficulties in the mining environment. Initially, integrating point-line characteristics during feature extraction significantly improves the algorithm's flexibility in poorly textured areas.