## 2 Literature Review
### Creating Indoor Maps for Robot Localization  
This section reviews studies that have created environment maps that can be used for robot operations, not limited to localization. Numerous studies have proposed different techniques and approaches using the appearance, geometric, and topological features contained in the environment for creating a two-dimensional (2D) map and localizing robots in spaces. A statistical approach was proposed in [30] for creating 2D metric maps based on landmark features for large-scale indoor environments using the motion and perception of a mobile robot. The approach can estimate both the robot's position and mark the location of the landmarks in an indoor environment to create a map. However, the approach requires a human to identify these landmarks and control the robot. The authors of [31] created 2D maps that contain both topological and geometric features by performing SLAM and localizing the robot using the topological features or objects in an unknown space. This approach may experience difficulties in an open space with little topographical features (e.g., a plain wall with no edges) that the robot can recognize easily. Ref. [32] presented a multilayered map by first creating a 2D metric map with geometric information and transforming the data into a topological map. The transformation process described in the study adjusts the potential obstacle grid of the metric map to create topological features of the environment. The authors of [33] presented a view-based mapping system to incrementally build maps with geometric features for both outdoor and indoor environments as the robot performs SLAM in the environment. In this approach, a map was generated by continuously stitching camera views using geometric features. Ref. [34] proposed a loop-closure detection approach using a memory management mechanism to reduce the time to create maps for large areas. Similar to that in reference [33], the loop closure detection allows a robot to link previously seen views to the current view in real time during SLAM to create a geometric map. In [35], a graph-based SLAM solution was proposed to create geometric maps for indoor environments based on the memory management approach in [34] to cater to the multi-session mapping of large areas where gross errors in odometry and the \"kidnapped robot\" situation might occur. Even though several studies have created 2D geometric maps for robot task planning and navigation, recent studies have attempted to incorporate important non-geometric information about the environment in maps to facilitate the intelligent behaviors of robots. Many studies proposed methods of combining semantic information with geometric and topological features of spaces to further improve a robot's understanding of a space for performing tasks. Ref. [36] proposed a multi-hierarchical approach using sensor data from a mobile robot to build semantic maps that includes both spatial and semantic information of spaces for performing tasks, such as room classification according to observed objects. This multi-hierarchical approach consists of a metric map connected to global topological features and is linked to the semantic representations of the space through an anchoring process that allows the robot to draw inferences from symbolic data to perform complex tasks that requires semantic information. [PERSON]. [37] extended the approach in [36] to present ways in which a robot can generate new goals based on predefined semantic knowledge in the metric map to deduce new information for performing complex task planning and ignore less important information. Similar to that in [36], a Bayesian hierarchical probabilistic approach was presented in [38] to classify and infer semantic information about a space by linking commonly found objects and spatial features in that space to create a semantic map with spatial features. Ref. [39] acquired 3D scan data of the spatial features of spaces by performing 6D SLAM with a mobile robot to create a 2D semantic map.