Compensating Field-of-View Constraints for Decentralized Indoor Localization Using Multi-Pose Techniques

Positioning technology is the key of various fields such as mobile robots and intelligent vehicles. Lots of applications need to obtain indoor and outdoor positioning data continuously when the environment of themselves changes. This paper proposes an indoor and outdoor seamless positioning method for open environment based on the adaptive Federated Filter (FF). Rather than most of the existing indoor positioning methods which need to add extra equipment to the positioning target, this method uses target tracking algorithm to provide multi-target indoor positioning and uses data association method to confirm the target identity. In addition, two sub-filter built by Inertial Measurement Unit (IMU) and Global Navigation Satellite System (GNSS), IMU and indoor positioning respectively are integrated to construct an adaptive FF to realize the indoor and outdoor seamless positioning. An indoor and outdoor seamless positioning system consisting of multiple mobile units and indoor positioning units is also designed to implement the method expediently. The experiment results show that the indoor positioning method proposed in this paper is greatly improves the applicability of indoor positioning services and lightly better than the ultrawideband (UWB) and IMU fusion positioning method, and the indoor and outdoor seamless positioning method can obtain stable and smooth positioning data in the whole positioning process.

Abstract. Localization can be defined as the process of determining the position and orientation of an entity within an environment, that would enable it to navigate and carry out tasks effectively. It is of fundamental importance for a wide range of areas such as robotics, medicine, indoor and outdoor navigation, autonomous vehicles, etc. Localization problems might be solved either with hardware or software designs. However, considering the challenging environments that would need a localization process, hardware designs might be complicated to apply to this problem. Indoor navigation can be shown as an example of these challenging environments since classical positioning methods cannot be used in such places. In this case, visual localization might be a solution since it requires either monocular or stereo images taken through the path. It would be a time-saving and cost-effective way to determine the locations that images were taken, thus the path that a robot or medical instrument, etc. take along the way. In this case, it is important the determine the performances of different localization algorithms. In this study, the performance of two different localization algorithms in indoor navigation was tested. In this context, Visual odometry and EKF SLAM algorithms were used to determine the camera trajectory utilizing the images that were taken in a straight corridor with a smartphone camera. To determine the accuracy of each method, the distances between each image-taking point were measured and compared with the distances obtained from the algorithm. Thus, root mean square error values were determined by each method. The precisions of each method were also given based on the fact that the distance between each image-taking point was equal. Therefore, the usage of both algorithms in indoor navigation was discussed.

Innovative technologies have been incorporated into intelligent transportation systems (ITS) to improve sustainability, safety, and efficiency, hence revolutionising traditional transportation. The combination of three-dimensional (3D) indoor building mapping and navigation is a groundbreaking development in the field of ITS. A novel methodology, the “Three-Dimensional Routing Information Framework “(3D RIF), is designed to improve indoor navigation systems in the field of ITS. By leveraging the Quantum Geographic Information System (QGIS), this framework can produce three-dimensional routing data and incorporate sophisticated routing algorithms to handle the complexities associated with indoor navigation. The paper provides a detailed examination of how the framework can be implemented in transport systems in urban environments, with a specific focus on optimising indoor navigation for various applications, including emergency services, tourism, and logistics. The framework includes real-time updates and point-of-interest information, thereby enhancing the overall indoor navigation experience. The 3D RIF’s framework boosts the efficiency and effectiveness of intelligent transportation services by optimising the utilisation of internal resources. The research outcomes are emphasised, demonstrating a mean enhancement of around 25.51% in travel. The measurable enhancement highlighted in this statement emphasises the beneficial influence of ITS on the efficiency of travel, hence underscoring the significance of the ongoing progress in this field.

In recent years, indoor and outdoor positioning technology for the blind has attracted much more attention. The outdoor positioning technology has relatively matured. However, three-dimensional indoor positioning technology is facing many problems. Due to low positioning accuracy as well as slow positioning speed, there is no ideal implementation system at present. Light Fidelity (LiFi) technology is a new wireless transmission technology that uses the visible light spectrum, such as the light emitted by a light bulb, to transmit data. It transmits signals through the changing light of LED lights. When LED lights are properly laid out in the building, not only the function of lighting but also that of transmitting information are met at the same time. It is a good way to solve indoor positioning problems. This article studies an indoor positioning glasses for blind based on the LiFi technology. Different from other indoor positioning technologies such as ultrasound and wireless fidelity (WiFi), the design of indoor positioning glasses for the blind will propose new ideas of three-dimensional indoor positioning problems for the blind.

Services based on indoor navigation and localization play crucial roles in people's daily life, and they are even more important for individuals with visual impairments. In this paper, an indoor navigation and localization system CoFINLo with three key techniques: 1) dynamic feature extraction, 2) incremental model update, and 3) fine-grained trajectory generation, is proposed to implement robust indoor localization with friendly interaction design especially for visual impairment groups. Compared with other indoor localization systems, the proposed CoFINLo can overcome the obstacles caused by the diverse sensing devices, complex indoor environments, and fluctuated wireless signal. The proposed system is verified in an indoor office environment and implemented in a large-scale and comprehensive train station. The average 1.39m localization error suggests that CoFINLo can open a new opportunity to implement robust and user-friendly indoor navigation and localization in large and complex indoor environments, which can be a solid foundation for other location-based services.

A quickly growing location-based services area has led to increased demand for indoor positioning and localization. Undoubtedly, Wi-Fi fingerprint-based localization is one of the promising indoor localization techniques, yet the variation of received signal strength is a major problem for accurate localization. Magnetic field-based localization has emerged as a new player and proved a potential indoor localization technology. However, one of its major limitations is degradation in localization accuracy when various smartphones are used. The localization performance is different from various smartphones even with the same localization technique. This research leverages the use of a deep neural network-based ensemble classifier to perform indoor localization with heterogeneous devices. The chief aim is to devise an approach that can achieve a similar localization accuracy using various smartphones. Features extracted from magnetic data of Galaxy S8 are fed into neural networks (NNs) for training. The experiments are performed with Galaxy S8, LG G6, LG G7, and Galaxy A8 smartphones to investigate the impact of device dependence on localization accuracy. Results demonstrate that NNs can play a significant role in mitigating the impact of device heterogeneity and increasing indoor localization accuracy. The proposed approach is able to achieve a localization accuracy of 2.64 m at 50% on four different devices. The mean error is 2.23 m, 2.52 m, 2.59 m, and 2.78 m for Galaxy S8, LG G6, LG G7, and Galaxy A8, respectively. Experiments on a publicly available magnetic dataset of Sony Xperia M2 using the proposed approach show a mean error of 2.84 m with a standard deviation of 2.24 m, while the error at 50% is 2.33 m. Furthermore, the impact of devices on various attitudes on the localization accuracy is investigated.

In recent years, service robots have been widely used in people’s daily life, and with the development of more and more intelligence, people put forward higher requirements for autonomous positioning and navigation functions of robots. Like outdoor navigation, indoor navigation also needs the support of navigation data. Although the indoor positioning and navigation scheme based on cameras, lidars and other sensors is gradually developing, due to the complexity of the indoor structure, manual production of indoor navigation data is time-consuming and laborious, and the efficiency is relatively low. In order to solve the problem of low productivity and improve the accuracy of robot automatic navigation, we added a new type of intelligent camera, called OpenCV AI kit or OAK-D, and proposed a method to automatically build data files that can be used for indoor navigation and location services using indoor 3D point cloud data. This intelligent camera performs neural reasoning on chips that do not use GPUs. It can also use stereo drills for depth estimation, and use 4K color camera images as input to run the neural network model. Python API can be called to realize real-time detection of indoor doors, windows and other static objects. The target detection technology uses an artificial intelligence camera, and the robot can well identify and accurately mark on the indoor map. In this paper, a high-performance indoor robot navigation system is developed, and multisensor fusion technology is designed. Environmental information is collected through artificial intelligent camera (OAK-D), laser lidar, and data fusion is carried out. In the experiment part of this paper,The static fusion map module is created based on the laser sensor information and the sensor information of the depth camera, the hierarchical dynamic cost map module is created in the real-time navigation, and the global positioning of the robot is realized by combining the word bag model and the laser point cloud matching. Then a software system is realized by integrating each module. The experiment proves that the system is practical and effective, and has practical value.

Indoor and outdoor positioning system based on navigation signal simulator and pseudolites

Indoor navigation systems (indoor positioning systems) can improve orientation for patients in hospitals and help employees to track assets. Many hospitals would like to implement indoor positioning systems but do not know how. To support them in doing this, and to gain knowledge about the requirements for indoor positioning system implementation, our research identifies the design criteria relevant to indoor positioning system implementation projects. A design science research process is built to design and evaluate an artifact. For this, five indoor positioning system developers and five hospital IT management representatives from various hospitals and companies in Germany are interviewed. Further, controlled experiments are conducted in Germany, using an ultrasound-based indoor positioning system. We determined and tested indoor positioning system functions, evaluated indoor positioning system performance criteria, and identified the operating conditions in hospitals. Our results show that indoor positioning system functions should provide a benefit to a hospital's daily operations, that some performance criteria are more important than others, and that operating conditions are important, e.g., radiation. As a theoretical contribution, we show how design science research can be applied to the context of indoor positioning systems in hospitals. In addition, we make a practical contribution in that our propositions can be used for future indoor positioning system developments.

The indoor positioning system (IPS) has been attracting great attention from researchers, thanks to the rapid adoption of smartphone technologies. Although there are many IPS proposed in the past decade that claimed to have good performance, all of them use their own method to evaluate and compare the accuracy of the proposed solution. During the evaluation phase, the method of gathering ground truth data (original position) is often not well described. As such, it is very difficult for other researchers to reproduce the work and improve on the existing methods. In this paper, we proposed a simple to implement framework to facilitate the process of evaluating IPS accuracy. Under this framework, the IPS position coordinates and ground truth are sent to the server using REST protocol when the phone reads an event triggered from tags scan placed on a fix position. We evaluated an existing well-known IPS technique, the Pedestrian Dead Reckoning (PDR) technique using our IPS evaluation framework. From our experiments, we showed that in addition to measuring the accuracy of IPS, the proposed solution can also measure the IPS accuracy deviation over time. Instead of relying on precision and recall, the framework also includes visualization tool for researchers to observe the overall accuracy of an IPS.

BackgroundIndoor navigation within closed facilities has been subject of studies with different application areas, particularly in recent years (e.g. the navigation requirements of people or the location of objects). Hospitals are of specific interest in this regard as the multitude of technical equipment used is potentially interfering with navigation systems.ObjectiveThis research examines relevant studies regarding Indoor Positioning Systems (IPS) in hospitals and IPS that are designed for hospitals and in preparation for implementation, by investigating the respective technologies, techniques, prediction-improving methods, evaluation results, and limitations of the IPS.MethodsTo gather current and future IPS in hospitals, the methodology of a Scoping Review was used. The study has been conducted by applying the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Framework to the context of IPS in hospitals. The results and limitations concerning current and future IPS in hospitals were gathered and structured by using a highly cited evaluation framework for IPS.ResultsThirty-eight studies were considered for this research. The IPS technologies investigated were Bluetooth Low Energy ( n = 17), Wireless-Fidelity ( n = 10), Hybrids ( n = 4), Radio-Frequency Identification ( n = 4), Ultra-Wideband ( n = 1), Infrared ( n = 1) and ZigBee ( n = 1).ConclusionsThis study presents current and future IPS in hospitals. For future IPS research and IPS in hospitals, the theoretical implications contribute to our knowledge about IPS technologies, techniques, prediction-improving methods, evaluation results and limitations during testing/implementing IPS in hospitals. As practical implications, the insights of this study can be used by developers to improve IPS and by hospitals to facilitate IPS implementation.

As humans, nearly eighty percent of our day-to-day activities are conducted in an indoor environment, making indoor tracking and monitoring extremely necessary. The needs of having an Indoor Positioning System (IPS) has become crucial as the development is critically challenged due to the fact that satellite signals via Global Positioning System (GPS) cannot penetrate through a building. Variety techniques and approaches on IPS have been proposed but the most desirable approach used is the Received Signal Strength Indicator (RSSI) due to existing infrastructure already in place to provide a low-cost implementation. Internet of Things (IoT) itself has brought drastic changes and opened new opportunities for growth and innovation in technology. An integration between RSSI and IoT is the perfect combination of the development of IPS to allow an increased accuracy. This paper proposes an Indoor Monitoring and Positioning System (IMPS) using RSSI algorithm and triangulation technique integrated with IoT. The experimental results show that the proposed IMPS has achieved an average accuracy of 0.5 m for 2D. Future work has suggested an improvement in the IMPS with AR mapping integrated with Machine Learning.

Building a precise low cost indoor positioning and navigation wireless system is a challenging task. The accuracy and cost should be taken together into account. Especially, when we need a system to be built in a harsh environment. In recent years, several researches have been implemented to build different indoor positioning system (IPS) types for human movement using wireless commercial sensors. The aim of this paper is to prove that it is not always the case that having a larger number of anchor nodes will increase the accuracy. Two and three anchor nodes of ultra-wide band with or without the commercial devices (DW 1000) could be implemented in this work to find the Localization of objects in different indoor positioning system, for which the results showed that sometimes three anchor nodes are better than two and vice versa. It depends on how to install the anchor nodes in an appropriate scenario that may allow utilizing a smaller number of anchors while maintaining the required accuracy and cost.

The localization and navigation systems play a very significant role in today’s world. They are commonly used in different fields of industry as well as in our daily life. Location of objects is crucial in logistics and transport to provide the real-time monitoring and management of process chain. There are numerous methods which allows to follow people and objects within defined area. One of them is Global Positioning System (GPS), which provides geolocation of objects on the earth as long as an unobstructed line of sight to four or more satellites is assured. As a consequence of this limitation, GPS cannot be applied for indoor environments. Recently, a couple of Indoor Positioning Systems (IPS) based on different technologies have been developed. Despite the drawbacks and limitations, these systems have been successfully applied for different fields of industry. Notwithstanding the above, reliable, accurate, cost-effective and simple indoor positioning methods are still in the area of scientists interest. Present paper provides a general overview of existing indoor positioning systems as well as authors concept of 2D wireless indoor positioning and navigation system dedicated for autonomous vehicles based on Radio Frequency Identification (RFID) technology.

With many different studies showing a growing demand for the development of indoor positioning systems, numerous positioning and tracking methods and tools are available for which can be used for mobile devices. Therefore, an interest is more on development of indoor positioning and tracking systems that are accurate and effective. Presented and proposed in this work, is an indoor positioning system. As opposed to an Ad-hoc Positioning System (APS), it uses a Wireless Mesh Network (WMN). The system makes use of an already existing Wi-Fi infrastructure technology. Moreover, the approach tests the positioning of a node with its neighbours in a mesh network using multi-hopping functionality. The positioning measurements used were the ICMP echos, RSSI and RTS/CTS requests and responses. The positioning method used was the trilateral technique, in combination with the idea of the fingerprinting method. Through research and experimentation, this study developed a system which shows potential as a positioning system with an error of about 2 m to 3 m. The hybridisation of the method proves an enhancement in the system though improvements are still required.