Technologies and techniques of location and navigation are advancing, allowing greater precision in locating people in complex and challenging conditions. These advances have attracted growing interest from the scientific community in using indoor positioning systems (IPSs) with a higher degree of precision and fast delivery time, for groups of people such as the visually impaired, to some extent improving their quality of life. Much research brings together various works that deal with the physical and logical approaches of IPSs to give the reader a more general view of the models. These surveys, however, need to be continuously revisited to update the literature on the features described. This paper presents an expansion of the range of technologies and methodologies for assisting the visually impaired in previous works, providing readers and researchers with a more recent version of what was done and the advantages and disadvantages of each approach to guide reviews and discussions about these topics. Finally, we discuss a series of considerations and future trends for the construction of indoor navigation and location systems for the visually impaired.

Electronic indoor positioning systems deal with the combination of sensors, actuators, and computational algorithms for precisely locating subjects, delivering navigation directives, and keeping track of particular objects. The main factors considered for the construction and evaluation of these systems are the localization accuracy and the time spent to calculate and deliver this information. The challenge in developing successful positioning systems is to find a tolerable relationship between those factors. In this proposal, after a careful analyses of related works, we associated different methodologies and technologies to construct a hybrid positioning model that uses a mapping algorithm called Linear Weighted Policy Learner, a navigation model called iterative Pedestrian Dead Reckoning (which uses the Kalman filter to deliver real-time location), and an obstacle detection algorithm that combines sounds and stereo vision sensorial capabilities. The adopted choices were based on the published state-of-the-art, and comparisons of the obtained results showed that our system is accurate and fast enough to be very competitive with the current stage of the technology.

Indoor navigation systems offer many application possibilities for people who need information about the scenery and the possible fixed and mobile obstacles placed along the paths. In these systems, the main factors considered for their construction and evaluation are the level of accuracy and the delivery time of the information. However, it is necessary to notice obstacles placed above the user’s waistline to avoid accidents and collisions. In this paper, different methodologies are associated to define a hybrid navigation model called iterative pedestrian dead reckoning (i-PDR). i-PDR combines the PDR algorithm with a Kalman linear filter to correct the location, reducing the system’s margin of error iteratively. Obstacle perception was addressed through the use of stereo vision combined with a musical sounding scheme and spoken instructions that covered an angle of 120 degrees in front of the user. The results obtained in the margin of error and the maximum processing time are 0.70 m and 0.09 s, respectively, with obstacles at ground level and suspended with an accuracy equivalent to 90%.