## 2 Literature Review
### Shopfloor Perception  
Nowadays, vision sensors are used extensively in shopfloor perception for navigation or localization of the mobile robot. They can be used either on their own or in combination with other sensors, such as laser scanners, to achieve more accurate results.  
Despite recent advances in the area of navigation, there are still open questions and uncertainty, especially regarding navigation in unknown environments, which has been researched in the last decades with several approaches, such as fuzzy-set-based representation [18]. Topological localization and world modeling are considered effective solutions for uncertainty in position, but the improvement of perception capabilities to recognize objects in the environment also has been suggested [19].  
The many types of sensors used for mapping, localization and navigation [20] that measure external information from the environment include GPS, SONAR, lase and infrared sensors. Odometry is calculated from encoders installed on the wheel, but they accumulate errors over time and need to be supplemented with other sensor data. GPS is a good choice for outdoor use, but does not work as well indoors, close to buildings or on narrow streets.  
SONAR can be utilized as a low-cost system that is effective in spaces with low visibility or with other difficulties such as being surrounded by mirrors [21], but its precision is low. Laser scanners are often used in combination with odometry for navigation of mobile robots.  
Vision sensors present an alternative for the above sensors, or a complementary measure to ensure accuracy in real-time applications. Laser, ultrasonic or infrared sensors give data in an imperceptible way, from light radiation or sound signals transmitted from a source mounted on the robot, for example [22]. The system could produce inaccuracies due to the interruption of the traveling signal caused by disturbances in the surrounding environment. Vision sensors are also not very power-consuming and usually cost less than their lase counterparts, providing a lot of information, including 2D and 3D data, depending on the sensor. They can also be used both indoors and outdoors, although they suffer from light variation, and great care should be taken in the choosing of the appropriate camera.  
In [23], a method was proposed for real-time indoor robot localization, using a stationary fisheye camera not mounted on the robot, but instead installed on the ceiling of the room, although the localization was not very accurate, especially when the robot was further away from the camera.  
In [24], a single camera mounted on a mobile robot was used for path generation of the robot. Floor segmentation was used to identify the free space on the floor, while a laser source was used complementary, in order to detect obstacles and irregularities, such as a hole in the floor. The depth estimation was calculated using the camera, not a laser.  
SCARF, a color vision system, was developed in [25] to track roads and intersections and for navigation in a variety of weather conditions. It used Bayesian classification to determine road-surface likelihood for each pixel in a color image.