# Semantic Information for Robot Navigation: A Survey
## 2 Acquisition of Semantic Information in Robot Navigation
### Fully Automated Information Acquisition
#### 2.2.1 Understanding the Environment
A recent work proposed a vision-based three-layer perception framework based on transfer learning for mobile robots during semantic navigation [56]. This proposal includes a place recognition model to distinguish different regions in rooms and corridors. Apart from those systems that perform object recognition and classification using visual information, the work of [PERSON] et al., proposed using laser range-finder measurements for semantic labelling of places [57]. Observations are classified by applying a sequence of binary classifiers based on AdaBoost. [PERSON] et al., adapt techniques normally used in computer vision to laser range measures to distinguish between rooms and corridors using SVM [58]. Additionally, a labelling approach based on a multi-sensory system was described in [PERSON] et al. [59]. This system combine visual cues with laser range data with a SVM classifier to distinguish between indoor environments (corridor, office and meeting room). At this point, it should be clear that mobile robot navigation can benefit from adding high-level semantic knowledge. Topological navigators can be generated from the results of classifiers labelling the nodes of the map. Also, semantic navigators that use the methods presented in this work can be built on a topological or geometric navigator. [PERSON] et al., used data from a system that returns a plane of 360 degrees to distinguish between room, hallway, door and entrance hall [60]. To achieve this, the system only used geometric data. In contrast, many proposals use computer vision to detect and recognize objects for place labelling. For instance, [PERSON] et al., used Haar features to get the number of specific objects present in the environment to perform Semantic Place Classification of Indoor Environments With Mobile Robots [61]. Different categorizations of places were discussed in [PERSON] et al. [62], dividing the classifications of indoor places into two types:  
* Indoor single scene interpretation. Different works converge here. [PERSON] and [PERSON] proposed a framework to perform semantic annotations of RGB-D data [63]. Authors used the implementation of a Random Forests classifier to group the scenes, and SVM to predict objects and indoor scenes. The model is based on Conditional Random Field (CRF) to provide unary features. Another approach that is also supported by CRF and Random Forests is presented in [PERSON] et al. [64], while in [PERSON] et al. [65] authors proposed segmenting the scene into fragments of neighbouring 3D points. Having in mind low-level features such as textures or normal entropy, researchers manage to differentiate the areas of the scene that change over time from those that are static. This makes it easier to recognize places when the robot returns to visit a place. * Indoor large scale interpretation. This category includes works such as the proposal of [PERSON] et al. that proposed a method for semantic segmentation of 3D scenes in different places [66]. The 3D reconstruction process is carried out by adding new scenes to those previously acquired. The locations are then labelled with distance, colour, and normal orientation information. [PERSON], proposed an online method that segments RGB streams and labels inferring the parameters in a Bayesian model [67]. Besides, methods of place recognition supported by binary characteristics descriptors are suggested [68]. This represents a tendency to categorize places using statistical hypotheses. [PERSON] et al., proposed a place recognition system for UAV navigation that combines 2D and 3D information [69]. This approach codifies the information into a hierarchical Bag of Binary Words visual vocabulary. In [PERSON] et al., authors presented a framework that includes cost maps in layers, each one of these maps has a distinctive semantic meaning [70]. However, it is quite widespread that researchers join topological or geometric maps with data from objects located in the environment. [PERSON] et al., combined a metric map with objects in space [71].