Identifying Indoor Objects Using Neutrosophic Reasoning for Mobility Assisting Visually Impaired People

Abstract

Indoor object detection is a fundamental activity for the development of applications of mobility-assistive technology for visually impaired people (VIP). The challenge of seeing interior objects in a real indoor environment is a challenging one since there are numerous complicated issues that need to be taken into consideration, such as the complexity of the background, occlusions, and viewpoint shifts. Electronic travel aids that are composed of the necessary sensors may assist VIPs with their navigation. The sensors have the ability to detect any obstacles, regardless of whether they are static or dynamic, and offer information on the context of an interior scene. The characteristics of an interior scene are not very clear and are subject to a great deal of variation. Recent years have seen the emergence of methods for dealing with issues of this kind, some of which include the use of neural networks, probabilistic methods, and fuzzy logic. This study describes a method for detecting indoor objects using a rotational ultrasonic array and neutrosophic logic. A neutrosophic set has been seen as the next evolution of the fuzzy set because of its indeterminate membership value, which is absent from conventional fuzzy sets. The suggested method is constructed to reflect the position of the walls (obstacle distance) and to direct the VIP to move freely (ahead, to the right, or to the left) depending on the degree of truthiness, the degree of indeterminacy, and the degree of falsity for the reflected distance. The results of the experiments show that the suggested indoor object detecting system has good performance, as its accuracy rate (a mean average precision) is 97.2 ± 1%.

A Multi-Sensor Fusion Approach Based on PIR and Ultrasonic Sensors Installed on a Robot to Localise People in Indoor Environments

“…Moreover, people may not want their movements to be tracked, especially in their own homes, and ensuring privacy while still accurately localising occupants poses a significant challenge [ 6 ]. With regards to the former, the most common methods used to deal with the ambiguity problem in localising occupants in indoor environments are: Multi-Sensor Fusion approaches [ 5 , 6 , 7 , 8 ]: They integrate data from multiple sensors such as cameras [ 9 , 10 , 11 , 12 ], microphones [ 13 ], passive infrared motion sensors (PIR), LiDAR, and ultrasonic sensors to improve the accuracy of the localisation system. By using data from multiple sensors, the system can reduce the ambiguity in location estimation and provide more reliable and accurate results.…”

“…Multi-Sensor Fusion approaches [ 5 , 6 , 7 , 8 ]: They integrate data from multiple sensors such as cameras [ 9 , 10 , 11 , 12 ], microphones [ 13 ], passive infrared motion sensors (PIR), LiDAR, and ultrasonic sensors to improve the accuracy of the localisation system. By using data from multiple sensors, the system can reduce the ambiguity in location estimation and provide more reliable and accurate results.…”

A Versatile Approach to Polygonal Object Avoidance in Indoor Environments with Hardware Schemes Using an FPGA-Based Multi-Robot

“…Sensory information and its fusion play a vital role in evaluating the environment. Sensors like infrared, ultrasonic, PIR, LIDAR, LASAR, and cameras have been used by various researchers [3,4] for the validation of their research. There is a tradeoff between the sensor types, on the one hand, and cost and computational challenges, on the other.…”