## 4 Comparison of Systems Discussed
### Application of Techniques and Technologies in Visually Impaired Navigation
The inertial sensor evaluates the user's head movement (direction, speed) to provide angle correction parameters for the computer vision algorithm. The device, mounted on the user's head, made it possible to perceive objects distant from the user about 0.15 m, using images with a stereo resolution of \(2\times 1280\times 720\) pixels, obtained at 15 frames per second (fps). Many authors have also chosen to combine computational resources to make indoor navigation and location solutions adaptable to the context of the scenario. [PERSON] received collaboration from a team of collaborators to develop an indoor navigation model that would approximate the models adopted in outdoor scenarios [127]. The authors named the IndoorWaze system, which recognized the context based on crowdsourcing, increasing the level of knowledge of the scenario, using data previously mapped with data generated and labeled for the first time, provided collaboratively by other users. They submitted the two sets of data to the same fingerprinting map to represent the floor plan of building structures. The high complexity and cost involved in preparing maps for large environments such as shopping malls, condominiums, and university fields motivated the authors to use data provided collaboratively by users. The authors indicate that the maps enriched with data provided by the users generated a high-fidelity floor plan, with average error margins of 12%. [PERSON] et al. formed a research group on the location and indoor navigation of people with visual impairments [128; 129]. The group's two main research objectives were: to combine techniques and technologies to create records of reference sites to be used in the development of navigation routes and to create safety perimeters around the user to avoid collisions with obstacles. Another approach for indoor navigation that detects obstacles in the path was proposed by [PERSON] et al. by the combination of stereo vision with a sound musical language. They varied the musical notes A+ and C+ in three tones (octaves) between treble, medium, and bass in the horizontal and vertical planes, providing the user with a 360-degree sound sensation [128] (Figure 8). The main idea of the work was to create a context-sensitive visual obstacle detection approach to help people with visual impairments to navigate indoors. The obstacle perception model uses a resolution of \(320\times 240\)at 10 frames per second (fps). This resolution allows effective ranges of about 4.00 m horizontally and a view of 2.50 m vertically when the camera is positioned on the user's head facing forward and tilted about 45 degrees down from the point of the user's point of view. They conducted the experiments in indoor environments with different characteristics, such as straight, curves, obstacles, low, medium (height of the user's waist), and high (height of the user's head). The error margins in the horizontal plane were 0.33 m and in the vertical, 0.20 m. In this experience, 95% of users rated the experience as excellent or good, while 5% of users indicated that it was difficult to follow the sound instructions. Figure 9 illustrates the spatial recognition of the area around the user, indicating the viability of the path described by the guide and the possible presence of obstacles. In the navigation research that used records, the authors established a mapping model that allowed the inclusion, editing, and removal of new locations without the need to redo the entire arrangement. They adopted a technique called Linear Weighted Policy Learner (LWPL), which takes advantage of the characteristics of the Weighted Policy Learner (WPL) algorithm. Still, it introduces a linearization of its formulations to consume less time and processing [129]. Each record is the result of the fusion of data from Wi-Fi signals (RSSI), inertial sensors, and images (computer vision). For the navigation process, a RAndom SAmple Consensus (RANSAC) filter was used to reduce the data  
Figure 8: Obstacle detection scheme using stereo vision.