# 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 navigation, the proposed algorithm assumed that each room was built with walls and that if no walls were detected some virtual ones were accepted. If the robot crossed a wall, a new room was spawned, eventually generating an entire floor map with the robot's localization awareness. Mixed sensor readings were found as a second subtype from the Feature Set. In 2015, [PERSON] et al. (2015) presented a system based on two devices working together--a smartphone in the person's pocket and a foot-mounted IMU. Smartphone sensors provided data for the recognition of specific user actions, such as sitting, standing still, or walking up stairs. IMU readings were used for trajectory calculation and walked-path recognition. The actual room segmentation was carried out as in the floor plan's processing--using a location heatmap, which segmented parts of the user trajectory. The proposed solution achieved the subroom accuracy of localization in a multi-story building. Another system was created by [PERSON] et al. (2016) in 2016. They proposed to use multiple smartphone sensors to acquire data--camera, accelerometer, gyrometer, compass, magnetometer, and even Wi-Fi. The entire methodology consisted of two parts: offline training and online localization. First, a CNN model was trained for indoor scene classification, and a database of trajectories was constructed. Later, pictures from the camera were classified for the initial localization proposal. Both data sources were fused by a filtering algorithm and predicted the final location. In the article by [PERSON] et al. (2019) from 2018, a similar set of inputs was extended with a floor map. Instead of a CNN, they based the solution on a Monte Carlo Localization (MCL) with Bayesian filtering. Aware of reading instability, the localization problem was described as system state estimation from a sequence of noisy measurements. Newly introduced floor map data were combined with a filtering process to reduce the influence of noise on results. The whole system was equipped with an additional method for recovering from localization failure. The **Robot Exploration** task was mentioned in seven found articles and based on two types of input data. The first paper, from 2015, by [PERSON] et al. [182], presented a solution for the real-time navigation of a humanoid robot. The method followed a human-like behavior: the robot first looked at a floor map of the environment, processed it, and then used it to navigate during the exploration. Map processing consisted of information segmentation, structural analysis, and semantic analysis, and the combined results were used as input to a neural system responsible for online navigation. Similarly, the processing of 2D images was found in two consecutive articles by [PERSON] et al. [129; 132]. The first one presented a Contour-Based Topological Segmentation of a grid map. In the approach, a Dual Space Decomposition (DuDe) algorithm was used for segmentation and later improved with its custom incremental version. The idea was that new scans were aggregated to the already processed map--only the contours coming from new data were modified, leaving the rest unchanged. This allowed achieving similar quality results faster than before. The later article proposed to use it as a part of an online algorithm called TIGRE. It combined Graph-SLAM features and described contour-based segmentation, graph processing, and real-time decision-making to implement an autonomous robotic exploration. The implemented solution formulated the problem as a traversal of all graph edges generated online during environment exploration. The achieved results have shown similar error estimations as the off-line algorithms requiring prior full-graph awareness. [PERSON] et al. [131], in their work from 2017, focused on a room-by-room coverage task. Their scenario was to improve how an autonomous cleaning robot explores the environment and its time optimization.