# Voxel-Based Navigation: A Systematic Review of Techniques, Applications, and Challenges
## 2 Review Methodology
### Literature Analysis and Classification  
From the final pool of 83 papers, these publications were published between 2010 and 2024, including 32 conference papers and 51 journal papers. The top three sources of these publications are the following journals: ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences (7), International Journal of Applied Earth Observation and Geoinformation (6), and Automation in Construction (4). Figure 3a shows the number of relevant articles published each year. The number of articles from 2010 to 2015 was relatively low and exhibited minimal fluctuation. From 2016 to 2018, there was a significant increase in the number of articles, and from 2019 to 2024, the overall number remained at a high level, indicating a continued growth trend. Figure 3: (**a**) Annual number of articles on the topic of voxel-based navigation. (**b**) Number of papers on different techniques. By referring to previous studies [23; 24; 25], we used VOSviewer for co-occurrence analysis of keywords, clustering, and visualizing 79 keywords that appeared at least three times, as shown in Figure 4. The size of each node represented the frequency of keyword occurrence, and the lines connecting two nodes indicated whether they co-occurred, with the line width reflecting the frequency of co-occurrence. These keywords were divided into 5 clusters, based on their association strength. Cluster 1 (colored in red) primarily involved topics related to drone navigation and sensor-based path planning. Core keywords included map, environment, sensor, path, UAV, planning, and obstacle avoidance. This cluster focused on drone path planning, sensor data fusion, and obstacle detection, with an emphasis on dynamic obstacle avoidance and path optimization in drone navigation. Cluster 2 (colored in blue) primarily involved topics related to 3D reconstruction, voxel modeling, and path navigation. Core keywords included voxel, navigation, point cloud, structure, and ground. This cluster emphasized 3D environmental reconstruction and voxel modeling, using voxelization of point cloud data to achieve spatial structure extraction and environmental representation while supporting robot path navigation in 3D environments. Cluster 3 (colored in green) primarily involved topics related to indoor real-time navigation and performance optimization. Core keywords include object, scene, accuracy, indoor environment, position, performance, and real time. This cluster emphasized precise object and scene recognition in 3D spatial modeling and navigation systems, particularly achieving high-accuracy object localization and navigation performance evaluation in indoor environments to enhance the navigation performance of robots or autonomous systems. Cluster 4 (colored in yellow) primarily involved topics related to algorithm development and optimization methods. Core keywords included algorithm, approach, time, accuracy, and density. This cluster emphasized algorithm design and method optimization in 3D modeling and path planning, focusing on time efficiency, accuracy improvement,  
Figure 4: Keyword co-occurrence analysis and clustering based on VOSviewer. and data density handling in algorithms, exploring efficient methods to improve the performance of environmental reconstruction, path optimization, and navigation tasks. Cluster 5 (colored in purple) primarily involved topics related to SLAM (Simultaneous Localization and Mapping) and real-time environmental mapping. Core keywords included slam, robot, real-time, and mapping. This cluster highlighted the application of SLAM technology in robot navigation, focusing on real-time environmental mapping and localization estimation, aiming to achieve efficient spatial mapping and localization synchronization, and optimizing robot performance in dynamic environments. Based on the above analysis, we first selected papers related to voxel navigation technologies.