# Improvement of the Sensor Capability of the NAO Robot by the Integration of a Laser Rangefinder
## 2 Materials and Methods
### Autonomous Navigation and \"Range Finder\" Sensors  
There are several critical issues concerning the autonomous navigation of a robot moving in an unknown environment, such as the accurate mapping of the surrounding area and the localization of each nearby object. This issue can be easily solved when the robot navigates an outdoor environment by exploiting the Global Navigation Satellite System (GNSS) signals. However, the solution must be accomplished using more complex indoor or underwater navigation algorithms where such a system is unavailable. In the most straightforward cases, the robot has complete knowledge of the environment in which it moves, for example, by knowing the map of the environment or by adding distinctive markers within it. However, on a more general level, this information is not available. In this case, the robot must increase its knowledge while navigating, trying to build a detailed model of the surrounding environment, until obtaining a map and locating itself inside it. These operations can be performed online without needing any a priori knowledge of the area and are usually accomplished using specific and highly performing algorithms, known as SLAM (simultaneous localization and mapping) [42; 43; 44]. These algorithms exploit different mathematical and probabilistic approaches to solve the SLAM problem, but in all cases, their performance strongly depends on the accuracy and reliability of the scene's measurements. As described above, the NAO robot is equipped with a sonar-type sensor used by the robot's navigation system to carry out obstacle avoidance procedures. Nevertheless, the limited action range of such a sensor and the long response time result in feeble performance that is unsuitable for providing the information usually exploitable by algorithms such as SLAM. Furthermore, as described above, the NAO's odometry cannot be effectively employed for this purpose either. The location of the cameras on the robot's head does not yield any overlapping areas because the design allows a continuous alternation of forwarding gaze and gaze on the feet. Consequently, it is impossible to implement stereopsis to evaluate the distances and dimensions of the objects visible in the acquired images. The extraction of such parameters from monocular images, although possible through proper algorithms [45], shows limited performance and a high computational cost that is difficult to sustain by the NAO processor. Consequently, using a rangefinder laser seemed necessary to improve the system's capabilities in its autonomous navigation. Most of the SLAM algorithms available in the literature are based on measurements obtained by odometry and/or scanning with laser rangefinders. Currently, two leading families of laser rangefinders (laser rangefinder--LRF) are commercially available. They differ in the measurement principle used, which are as follows:  
* Time of flight measurement;
* Measurement of phase shift. The former family is represented by systems where the distance value is obtained based on the flight time measurement. This class of sensors presents the best performances in terms of precision and reliability. Nevertheless, it contains sensors of sizes and weights that are unsuitable for small robots in most cases. Indeed, this sensor family is commonly used in robots with larger dimensions and is mainly designed for outdoor use. On the contrary, the latter sensor family is features techniques for measuring distances, which are obtained based on phase shift measurements. These sensors generally have a much more compact structure than the previous sensors and show, in addition to considerably lower energy consumption, reduced weight, and dimensions, which makes them more suitable for use in small-size robots mainly designed for indoor use. For these reasons, using a rangefinder laser seems to be necessary to improve the system's capability in its autonomous navigation.