# Methods and Applications of Space Understanding in Indoor Environment--A Decade Survey
## 5 Taxonomy of Accomplished Tasks
### Taxonomy Presentation
#### 5.1.5 Floor Plan Prediction/Generation  
The task of floor plan deduction from gathered input data was found in 27 articles spread over all the analyzed buckets of data types. As a visualization, in Figure 9 we present the generation of floor plans based on the processing of the recorded trajectory of a user. The first articles found were those by [PERSON] et al. [79], with their solution for the generation of interior layouts for precast concrete-based buildings, and [PERSON] et al. [8], who contributed by proposing an approach for translating data from a grid map into a topological one. In 2014, [PERSON] et al. published research on mobile device-based floor plan generation [77]. They proposed to use a crowd-sensed combination of vision and inertial data from mobile phones. [PERSON] and [PERSON] [176] published the first version of floor plan generation from laser range data and continued their work a year later [177]. The presented solution was based on \"wall-samples\" processing and worked with even multistory buildings. [PERSON] et al. [89] switched to working with freehand sketches. They procedurally generated a floor plan from a user-provided paper drawing. An article from 2016 by [PERSON] et al. [170] discussed indoor map creation from omnidirectional images. They captured one image per room and processed them to generate a map of the whole floor. Their later work [153] presented an extension that even allowed them to create a complete 3D floor plan. A much different approach was introduced by [PERSON] et al. and [PERSON] et al. [183; 184; 185]. They proposed basing the floor plan processing on a set of sparse observations, assumed constraints, estimated parameters, and stochastic reasoning. [PERSON] [151] constructed a system for a mobile floor-cleaning robot, which generated a segmented floor plan based on laser readings and a hemispherical camera directed at the ceiling. [PERSON] et al. [25] made a step towards classical techniques, but with modern data--they applied morphological processing to the captured point cloud of the environment. [PERSON] et al. [31] introduced a method for room segmentation from a raw, unstructured 3D point cloud, later used in 2018 by [PERSON] et al. [33]. They applied it to a semantic room labeling task based on RBGD images. [PERSON] et al. used their previous work on floor plan vectorization and proposed a framework for plan reconstruction from 3D scans [78]. [PERSON] et al. [71] evolved the solution to an automated approach for CAD-like plan generation. A similar evolution can be seen in the project of [PERSON] and [PERSON] [37] that proposed reconstructing the walls of analyzed buildings from their scanned point clouds, and a later variation of the method presented by [PERSON] et al. [44] focused on better results with lower user dependence. [PERSON] et al. [53]  
Figure 8: Visualization of the Floor Plan Vectorization task. An input raster image (**left**) is converted to a vector representation (**right**). Converted walls are marked with black lines, doors with blue lines, windows with green lines and joints with red circles. proposed a solution for furniture-free room mapping using a mobile robot equipped with multiple 3D LiDAR. They combined the source data acquired from vertical and horizontal laser scans. [PERSON] et al. [189] presented a method of creating floor plans using smartphone sensors and recorded user traces. In a 2020 article, [PERSON] et al. [83] proposed a solution for house layout generation according to an input bubble diagram (graph) of expected rooms and connections between them. Users could specify types of rooms as well as connections between them and obtain a set of proposed house plans. [PERSON] et al. [58] presented a novel technique for floor plan generation from point clouds using deep neural networks trained on purely synthetic data. The publication of [PERSON] et al. [204] introduced a solution relying on input data unseen before--radio frequency readings from sensors.