## 2 Related Work
Another navigation system concept using an RGB-D (Red, Green, Blue, and Depth) sensor has also been under development in recent years. The RGB-D camera offers area-based ground segmentation, and the RGB sensor also provides color and object detection. In this solution, the user interface is provided via audio map details and audio cues or guidance [38; 39]. A different approach is to use the cellular networking framework that allows mobile phones to connect to other people. The authors of [40] suggest that a simple way to locate mobile devices is to use Cell ID, which works in the majority of networks. Some studies [41; 42] propose a hybrid method using a combination of wireless local area networks, Bluetooth, and communication networks to enhance the performance of positioning indoors. However, such tracking is unstable and prone to substantial navigation errors due to the presence of towers and radio signal interference. As people who are visually impaired or blind are more sensitive to hearing than others, a natural approach to navigation systems has been developed that sends warnings by voice and vibration [43; 44]. Depth sensors produce images typically interpreted by individuals through vision and tactile feedback. These systems convert spatial data into sound, leveraging sound's precise guiding capability. Many methodologies in this domain draw inspiration from audio surrogate devices, which encode visual scenes captured by a video camera and generate sounds as an auditory representation referred to as a \"soundscape\" [45; 46]. [PERSON] et al. [47] introduced a system that enhanced navigation without visibility, employing a guidance system based on the distinct advantage of virtual auditory guidance over spatial language. In [48], an image recognition method for blind people using sound was presented, offering a straightforward yet efficient approach to perceiving the world through auditory cues. However, as image complexity escalates, image recognition via auditory processing becomes challenging. Initially, the sound is eliminated using Gaussian blurring. Subsequently, image edges are filtered by identifying gradients. In the third step, non-maximum suppression is employed along the contours of the image edges. Thresholding values are then determined utilizing an edge detector. Once complete edge information is acquired, a sound is generated. Voice-controlled navigation systems provide alerts about the user's current position and travel directions [49; 50; 51]. The challenge with these types of systems is that they are unable to detect and warn of obstacles [51; 52]. One of the indoor navigation systems that applies a microcontroller for obstacle detection and a feedback system that uses voice donor technology to warn users of obstacles is the Roshni system [53; 54]. The user position in Roshni is identified using sonar technology, which is used to mount ultrasonic modules on the ceiling. Roshni is portable, can be moved to any location, and is not influenced by variations in the environment. It requires a precise map of the building's interior, which restricts it to navigating inside. Systems based on ultrasonic sensors usually consist of a built-in microcontroller with synthetic speech playback and a personal device that navigates users to their destination [55]. These systems use the principle of high-frequency bouncing to locate obstacles. Verbal instructions or directions are provided in vibrotactile form to reduce the difficulty of navigation. A limitation of such systems is that ultrasonic signals are blocked by walls, resulting in less accurate navigation [56]. Ultrasonic sensors first receive visual cues and then transform them into acoustic feedback. These systems can reduce the amount of learning required to use a white cane. However, the problem lies in the global identification of the user's location [57]. RFID (radio frequency identification) is yet another category of wireless technology used to aid visually impaired people's activities.