# Methods and Applications of Space Understanding in Indoor Environment--A Decade Survey
## 5 Taxonomy of Accomplished Tasks
### Taxonomy Presentation
#### 5.1.10 Indoor Navigation  
During the analysis, we split the indoor navigation task into four subcategories. Typically, the indoor navigation task achieved some variation of user guidance in environment discovery. Depending on the exact task, it could take the form of path planning from point to point (scenario visualized in Figure 14), user localization, robot navigation in an unknown environment, or extension of building visualization with Augmented/Virtual Reality (AR/VR). **Localization** was the first subtask analyzed. We found four main groups of data types to be in use. Multiple solutions based their processing on more than one input type and combined the results to improve performance. Received Signal Strength (RSS) in a Wi-Fi network was one of the data types from the Feature Set group. In 2014, [PERSON] [199] discussed the problem of concerns while using machine-learning algorithms in W-LAN fingerprinting. In the test scenario, three different smartphones were used to collect test samples, and multiple machine-learning approaches (KNN, SVM, Naive Bayesian) were tested in the room labeling task. The reported results indicated no significant influence of the data preprocessing on the end results. Similar research was carried out by [PERSON] et al. [93]. In the article found, data types from the 2D Images bucket were used--floor plan analysis was presented to support the Adaptive Indoor Wi-Fi Positioning System known from [217]. The radio-based solution collected RSS measurements and extracted mobility patternsand used them to create graphical and radio maps of the environment, later used to locate new samples. The proposed floor plan analysis extended the work with semantic meaning (room numbers) and simplified indoor navigation. A combination of floor plan analysis and radio fingerprinting was also found in the article of [PERSON] et al. (2000) from 2020. They focused on the crowd-sourced processing of location data accumulated over time. In their method, a floor plan was dynamically segmented based on the detected radio fingerprints and generated radio map. Detected locations were clustered together, creating a prediction of rooms, updated as the amount of data increased and adjusted with a parameter describing the sensitivity to individual areas. The point cloud was the subtype representing the 3D Spatial Data bucket. Although the data type suggested fully spatial input, we discovered that, in all three articles, the actual processing was based on RGBD images converted to point clouds. In the article by [PERSON] et al. (1966) from 2016, a wearable mobile device was used by a person with visual impairment to orientate in the environment. The semantic localization was deduced from a 3D indoor environment map created with the use of a Kinect sensor. Room classification performed by CNN gave the initial idea of the location and was corrected by the detection of representative objects--characteristic of specific rooms. The same year, an article by [PERSON] et al. (2021) presented a more universal work, a point cloud library-based framework for semantic localization. The implemented method relied on a Bag Of Words (BoW) technique. Input RGBD images were searched for valuable features, which created a dictionary of 3D words, later used to generate descriptors. Many configurations were tested, and an SVM with a combination of Harri3D (as keypoint detector) and PFHRGB (as feature extractor) was reported to score the overall highest location accuracy. The newest article found by [PERSON] et al. (2019) proposed a full Simultaneous Localization and Mapping (SLAM) method. Their implementation processed two separated yet synchronized data samples for each analyzed timestamp--one from the RGBD sensor and one from the robot's odometry (position and orientation). An RGB image was searched for objects with the use of the YOLOv3 detector. The depth image was converted to a point cloud and used for wall detection and placing found objects in the space.