# 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  
High-level navigation systems need rich information to generate representations of the environment. Object recognition, people detection and classification techniques are therefore one of the pillars on which a semantic navigation is founded. Although exploring these techniques is not the main focus of this work, they are necessary to understand better how semantic navigators work. **Object and people detection**  
Semantic navigators integrate methods for object detection such as SIFT [30] combined with other methods used in [31], [32] or [33]. More specifically, the work of [PERSON] et al., proposed a mobile robot that autonomously navigates in a domestic environment [31]. This work pursued integrating spatial and semantic knowledge in a service robot, putting together SLAM and an object detection system to generate a high-level representation of the environment. The work presented in Ekvall and Kragic describes a computer vision system that is part of an autonomous robot system that performs pick-and-place tasks using programming by demonstration for automatic interpretation of the teacher's instructions [32]. The work of [PERSON] aimed at enabling a mobile robot, with a camera and a laser sensor, to navigate in an environment while looking for known objects [33]. The development of a system of active visual search [34], combining semantic tracks to guide the process of object search is also interesting. The proposal starts from an unknown environment and implements an exploration strategy that takes into account the task of finding an object. The planner adapts the behaviour of the search depending on the current conditions using an indirect search algorithm [35] (e.g., when looking for a cellphone, the robot will look for a table first as it it likely that a phone can be on top of it). The ability to detect and identify objects is often used in the task of places classification required by the semantic navigation. [PERSON] et al., presented a system of recognition of furniture to accomplish this task [36]. Semantic navigation, therefore, can get a direct benefit from place classification methods that allow finding the location of a particular object. [PERSON] and [PERSON] considered the problem of how background knowledge about the location of usual objects can be used to find an object in an unknown environment [37]. [PERSON] et al., proposed a system that allows a mobile robot to differentiate among objects in a scene and use that information in semantic navigation [38]. The requirements of objects detection in this field are less than those applied to handling and gripping objects [39] in terms of the precision of location. However, there are works where objects and their distances are used for the classification of places [40] or for assessing the presence of objects in a location to make inferences [41]. A recent trend in object detection is semantic segmentation, a paradigm that assigns a class label to the pixels in an input image [42]. To achieve this, classifiers such as Pyramid scene parsing network (PSPNet) [43] are integrated on the approach for guiding a robot autonomously relying only on visual information. Another work, proposed using semantic segmentation and detection masks as observations and use deep learning to learn navigation policies [44]. The proposal uses high-level semantic and contextual features in the segmentation process coupled with detection masks. Also, this system takes advantage from the fact that once an agent has been trained in similar environment, it can discover commonly encountered objects and contextual cues, which allows learning policies that are able to generalize to unseen environments. Together with object detection, the detection of people is a problem widely addressed. A complete system can be found in [PERSON] et al. [45] where authors performed bulk segmentation, detection of head and shoulders and a time refinement. Additionally, the people detection algorithm used in [PERSON] et al.