# Methods and Applications of Space Understanding in Indoor Environment--A Decade Survey
## 5 Taxonomy of Accomplished Tasks
### Taxonomy Presentation
#### 5.1.10 Indoor Navigation
A median filter, morphological closing operation, and a watershed algorithm applied to a distance-transformed map were used for region segmentation and feature extraction. The extended clutter removal process, region merging, and the environment presentation as a region graph were utilized for later planning of the cleaning path. The reported results indicated a reduction in mission execution time in both types of movement: cleaning and path following. A more general approach to the topic was presented by [PERSON] et al. [171] in 2018. In their article, the main focus was not directly on navigation but on the robot's learning process. As stated, the environment in which the robot functions is constantly changing, and a full retrain of a neural network is a highly time-consuming task. The presented work described several approaches to including new data in the trained model of a deep CNN. Two architectures of the system were  
Figure 14: Visualization of the Indoor Navigation task. Input consists of the selected points of start and end (**left**). As a result of processing, a proposed path is generated and presented as a set of instructions (**right**). proposed and tested in a set of twelve different experiments. The reported results indicated that although a pure CNN achieved the best accuracy, its training time disqualified this approach, and a combination of CNN and a naive classifier performed better. Another approach to segmentation based on graph analysis was presented by [PERSON] et al. [134] in 2018. The proposed method focused on solving the \"kidnapped robot\" problem, i.e., restoring the robot's true global localization after an incorrect one. The method first cleaned the map with the use of a median filter and clustered free space using the Iterative Self-Organizing Data Analysis Technique Algorithm (ISODATA) [218] and ray casting [219]. Each cluster's center was used as a graph node representation and merged accordingly to their calculated connections. Searching for complete graphs led to the final segmentation of the region. The newest article by [PERSON] et al. [156] from 2020 approached the topic from the unsupervised learning point of view. In their proposal, a robot exploring the environment was processing consecutive frames of the video and combining them into a graph structure; they were then clustered into communities according to the Louvain Community Detection Algorithm [220]. Communities re-clustered with the robot's odometry readings provided the final segmentation of frames and led to topological map creation, which could be used in semantic localization. The **Path Planning** subtask was found in two articles. Methods presented in both are similar in the processing procedure to some of the already described solutions. The article of [PERSON] et al. [70] was focused on processing point cloud data in a way similar to the plain room segmentation task: a topological map of the environment was created by segmenting the free space into a set of convex regions. This was achieved by point cloud voxelization, Truncated Signed Distance Fields calculation, and convex clustering, with the final clusters being merged into larger, enclosed areas (e.g., rooms). The Path Planning was accomplished with an A* searching performed on a topological graph, where the clusters were nodes, and their adjacency was represented as edges. [PERSON] et al. [133], in 2018, proposed basing a multi-strategy path planner on the idea of space accessibility. An occupancy grid map was processed to identify rooms and hallways (with a GVG and Conditional Random Field model), later represented as a region topological map. The proposed method combined the grid and topological maps analysis and tried to create a planned path so that the hallways were preferred to be used. This was achieved using [PERSON]'s algorithm, the mentioned GVG, and a custom implementation of the A* algorithm. The results were reported to be closely similar to the path selection of a human being.