## 2 Related Work  
In the literature, input modules used in indoor navigation systems are categorized into three classes depending on the navigation sensors employed: non-camera-based, camera-based, and hybrid [1; 2]. Non-camera-based systems perceive the environment and display it to the user using a variety of sensors. Camera systems rely mostly on a live camera feed or pictures to represent the surrounding environment. Hybrid systems include both forms of inputs. Common in industrial contexts, non-camera-based technologies are often utilized for robot navigation. These technologies have been presented as a solution to the challenge of interior navigation for the visually impaired. Visually impaired users of non-camera-based systems must carry the appropriate equipment (tag or tag reader, smartphone, or other receiver/transmitter device) when moving around their environment in order to be trackable. The electronic tags include data needed to search for further information in a database and to pinpoint the user's position on an environmental map. IR sensors for indoor navigation are typically used to determine the distance from an obstacle [27]. Numerous academics and researchers have a strong interest in real-time indoor object detection. The problem is to reliably and precisely recognize the item in an image or video. In general, interior settings vary from outside ones. Typically, indoor scenery consists of several backdrop pieces and decorations [27]. Various methods are reported in the literature for detecting and recognizing walls, holes, and descending and ascending stairs. Infrared sensors, laser sensors, and monocular cameras are common components in these systems [27; 28; 29; 30; 31]. Sensor-based ETA systems capture environmental data and communicate it to the VI user via a range of audible or haptic signals. Computer vision advancements have resulted in the fast development of assistance gadgets based on artificial intelligence for outdoor and indoor navigation. The authors were able to identify on-floor obstacles in real-time with no a priori training thanks to the use of traditional computer vision techniques, including color histogram representation and edge detection [1; 2; 3]. The technology violates the hand-free restriction imposed by VI users while being unobtrusive and cheap. In addition, it is unable to detect obstacles that hang over its path. Indoor object identification based on machine learning methods is used in many other famous works as well [32; 33; 34]. The real-time restriction is frequently not satisfied by this kind of technique, which is heavily reliant on processing resources and has a very high computational cost. Recently, deep convolutional neural network (DCNN) models have received a lot of interest for a variety of computer vision applications. This method has been used to recognize indoor objects [35; 36; 37; 38]. Feature extraction utilizing DCNN and posture estimation were used to solve the issue of indoor placement. Other navigational aids for the mobility of individuals with VIP have been developed by combining artificial vision with map matching and GPS [1]. Fuzzy logic was shown to be helpful in addressing navigational issues for those with visual impairments in a large body of research [39]. Fuzzy rules, for example, have been utilized by some academics to assign preferences to things depending on the features of the objects themselves. Another group of researchers used a sophisticated sensor logic system to create a mobile route guiding assistance for those who are blind or partially sighted. Fuzzy control principles and image depth were combined to develop a novel obstacle avoidance strategy. A new guiding system, the directed elliptical model with fuzzy logic, has been developed to monitor medium-range traffic conditions in real-time for visually impaired pedestrians [39; 40]. Using Bluetooth Low Energy (BLE) beacons, the authors in [10] investigated several indoor localization techniques.