## 5 Experimental Design and Results Analysis
### Construction of Dense Point Cloud Map and OctoMap
Dense point cloud maps have considerable advantages over sparse maps in geometric completeness and feature retention, effectively reproducing multi-level structures in office settings. In the fr2_desk  
Figure 17: The fr3_long_office sequence data: (**a**) sparse mapping; (**b**) dense mapping; (**c**) OctoMap. Figure 18: The fr3_walk_rpy sequence data: (**a**) sparse mapping; (**b**) dense mapping; (**c**) OctoMap. sequence, the dense map effectively illustrates the spatial arrangement of desks and chairs and their positional relationships. Conversely, the feature points in sparse maps are more scattered, hindering the accurate representation of the scene's structural information. The algorithm exhibits exceptional environmental sensing abilities in more demanding scenarios (such as fr3_long_office and fr3_walk_rpy). The fr3_long_office sequence, featuring extensive office environments, effectively reconstructs comprehensive spatial structures through a dense point cloud map, encompassing intricate arrangements of corridors and office desks. The fr3_walk_rpy sequence, which includes dynamic objects, demonstrates the algorithm's high mapping accuracy due to efficient feature screening techniques that substantially mitigate interference from dynamic elements, showcasing its robustness in dynamic contexts. Furthermore, maps produced post-octree conversion achieve effective spatial encoding while preserving the structural integrity of the environment. Color coding at varying altitudes quickly illustrates variations in scene elevation, offering an optimal environmental representation for robotic 3D navigation planning. In extensive environments, such as fr3_long_office, octree maps proficiently manage sensor noise by probabilistic modeling, producing distinctly layered and organized spatial representations. The resulting octree maps, derived from dense point cloud maps, enhance map storage efficiency via voxelized representation. Table 4 illustrates the variations in storage space across six distinct scene sequences utilizing the enhanced SLAM system. In indoor scenes fr1_desk and fr1_xyz, point cloud maps require 6.5 M and 7.3 M storage, respectively, compressed to 43.5 k and 45.7 k after conversion to octree maps. In more significant scenes, fr2_desk and fr3_long_office, point cloud maps occupy 29.8 M and 53.2 M of storage, with their corresponding octree maps necessitating only 203.2 k and 314.6 k. In the dynamic scene fr3_walk_rpy, the point cloud map occupies 5.5 M, reducing to 36.8 k after octree representation. The experimental data indicate that octree maps substantially minimize storage overhead via effective spatial encoding. Their hierarchical spatial partition structure exhibits distinct advantages in various aspects. In contrast, the normalized spatial representation structure offers an efficient computational framework for complex tasks, including environmental perception, path planning, and dynamic obstacle avoidance for mobile robots. The experimental results confirm the robustness and applicability of the enhanced approach across many contexts. The complete procedure, encompassing sparse characteristics, dense reconstruction, and subsequent octree representation, exhibits exceptional environmental awareness and modeling proficiency. This organized environmental representation technique renders octree maps especially appropriate for autonomous robotic navigation in intricate dynamic settings, indicating extensive application potential.