## 4 Comparison of Systems Discussed
### Application of Techniques and Technologies in Visually Impaired Navigation  
[PERSON] et al. researched the adaptation of indoor navigation systems for visually impaired people in the context of the scenario [123]. The authors' objective was to identify the limitations in the countless strategies for using sensory technologies and algorithms to minimize user interactions with the system and provide continuous services, and automated and intelligent recognition about monitoring the environment. The authors classified the solutions found according to their types of deficiencies, confirming the need to explore the topic further and offer more reliable solutions. Other authors, such as [PERSON] et al. and [PERSON] et al., take another classificatory approach to navigation and indoor location systems to assist visually impaired people [61; 71]. The authors indicate that many strategies can be grouped into two broad classes: the systems that use maps and the systems of free mapping. In models that use maps, the systems depend on the data obtained by the sensors to establish addresses that allow representing faithfully or approximately the environments, such as buildings, schools, and hospitals [124]. On the other hand, authors such as [PERSON] et al., [PERSON] et al., and [PERSON] et al. indicate that indoor environments have characteristics that make it difficult to read addresses through sensors and suggest as a solution strategies to perceive the scenario itself, within a radius of sensory vision [24; 52; 125; 124]. [PERSON] et al. created a hybrid tracking system for the Android platform to measure Wi-Fi signals and inertial data in different indoor settings [126]. The choice of using the smartphone was because of the possibility of using its sensors and its high availability among the visually impaired public. The authors' strategy was to use inertial localization to mitigate the inaccuracy of data localization results presented by wireless networks, increasing the system's reliability. In some places, where the absence of the Wi-Fi signal was perceived, the inertial location system would assume the role of providing the approximate position. The average error observed by the system was 15 degrees, indicating 0.80 m. In noisy environments or with metallic elements, the average error rises to 30 degrees, showing a margin of error of 3.0 m. The results showed that the hybrid location is robust in environments with the low noise level and may not be as robust in environments with the presence of metallic components that affect the operation of the magnetometer and the Wi-Fi sensor. A limit must be applied so that the system recalibrates the sensor in these cases to reduce this effect. [PERSON] et al. formed a research group with interest in developing navigation for people with visual impairments in indoor environments using inertial sensors and the camera available on smartphones [23]. The inertial sensors magnetometer, gyroscope, and accelerometer provided information about the magnetic location and the direction to be followed in navigation in a PDR algorithm. At the same time, the computer vision indicated positions for the recognition of visual elements, such as colored ribbons and painted lines on the floor. The system demonstrated good accuracy in the navigation of visually impaired users. However, it is strongly impacted by lighting fluctuations, mainly in open places and with the incidence of sunlight. [PERSON] et al. developed a project called Sound of Vision (SoV) to provide an indoor navigation guide for people with visual impairments [52]. SoV's premises were to meet a set of requirements such as being wearable, performing user guide operations in real time, respecting usability issues, and having a low acquisition cost. The navigation algorithm reconstructs the scene in its 3D aspects using a stereo camera (two cameras) to generate a safety area around the user associated with inertial sensors.