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In the last decade, the interest in Indoor Location Based Services (ILBS) has increased stimulating the development of Indoor Positioning Systems (IPS). In particular, ILBS look for positioning systems that can be applied anywhere in the world for millions of users, that is, there is a need for developing IPS for mass market applications. Those systems must provide accurate position estimations with minimum infrastructure cost and easy scalability to different environments. This survey overviews the current state of the art of IPSs and classifies them in terms of the infrastructure and methodology employed. Finally, each group is reviewed analysing its advantages and disadvantages and its applicability to mass market applications.

Positioning information greatly enhances the convenience of people’s lives and the efficiency of societal operations. However, due to the impact of complex indoor environments, GNSS signals suffer from multipath effects, blockages, and attenuation, making it difficult to provide reliable positioning services indoors. Smartphone indoor positioning and navigation is a crucial technology for enabling indoor location services. Nevertheless, relying solely on a single positioning technique can hardly achieve accurate indoor localization. We reviewed several main methods for indoor positioning using smartphone sensors, including Wi-Fi, Bluetooth, cameras, microphones, inertial sensors, and others. Among these, wireless medium-based positioning methods are prone to interference from signals and obstacles in the indoor environment, while inertial sensors are limited by error accumulation. The fusion of multi-source sensors in complex indoor scenarios benefits from the complementary advantages of various sensors and has become a research hotspot in the field of pervasive indoor localization applications for smartphones. In this paper, we extensively review the current mainstream sensors and indoor positioning methods for smartphone multi-source sensor fusion. We summarize the recent research progress in this domain along with the characteristics of the relevant techniques and applicable scenarios. Finally, we collate and organize the key issues and technological outlooks of this field.

High-precision indoor positioning technology is regarded as one of the core components of artificial intelligence (AI) and Internet of Things (IoT) applications. Over the past decades, society has observed a burgeoning demand for indoor location-based services (iLBSs). Concurrently, ongoing technological innovations have been instrumental in establishing more accurate, particularly meter-level indoor positioning systems. In scenarios where the penetration of satellite signals indoors proves problematic, research efforts focused on high-precision intelligent indoor positioning technology have seen a substantial increase. Consequently, a stable assortment of location sources and their respective positioning methods have emerged, characterizing modern technological resilience. This academic composition serves to illuminate the current status of meter-level indoor positioning technologies. An in-depth overview is provided in this paper, segmenting these technologies into distinct types based on specific positioning principles such as geometric relationships, fingerprint matching, incremental estimation, and quantum navigation. The purpose and principles underlying each method are elucidated, followed by a rigorous examination and analysis of their respective technological strides. Subsequently, we encapsulate the unique attributes and strengths of high-precision indoor positioning technology in a concise summary. This thorough investigation aspires to be a catalyst in the progression and refinement of indoor positioning technologies. Lastly, we broach prospective trends, including diversification, intelligence, and popularization, and we speculate on a bright future ripe with opportunities for these technological innovations.

The recognition of locomotion activities (e.g., walking, running, still) is important for a wide range of applications like indoor positioning, navigation, location-based services, and health monitoring. Recently, there has been a growing interest in activity recognition using accelerometer data. However, when utilizing only acceleration-based features, it is difficult to differentiate varying vertical motion states from horizontal motion states especially when conducting user-independent classification. In this paper, we also make use of the newly emerging barometer built in modern smartphones, and propose a novel feature called pressure derivative from the barometer readings for user motion state recognition, which is proven to be effective for distinguishing vertical motion states and does not depend on specific users’ data. Seven types of motion states are defined and six commonly-used classifiers are compared. In addition, we utilize the motion state history and the characteristics of people’s motion to improve the classification accuracies of those classifiers. Experimental results show that by using the historical information and human’s motion characteristics, we can achieve user-independent motion state classification with an accuracy of up to 90.7%. In addition, we analyze the influence of the window size and smartphone pose on the accuracy.

Traditional simultaneous localization and mapping (SLAM) system tends to operate in small-area static environments, and its performance might degrade when moving objects appear in a highly dynamic environment. To address this issue, this paper proposes a dynamic object-aware visual SLAM algorithm specifically designed for dynamic indoor environments. The proposed method leverages a semantic segmentation architecture called U-Net, which is utilized in the tracking thread to detect potentially moving targets. The resulting output of semantic segmentation is tightly coupled with the geometric information extracted from the corresponding SLAM system, thus associating the feature points captured by images with the potentially moving targets. Finally, filtering out the moving feature points can greatly enhance localization accuracy in dynamic indoor environments. Quantitative and qualitative experiments were carried out on both the Technical University of Munich (TUM) public dataset and the real scenario dataset to verify the effectiveness and robustness of the proposed method. Results demonstrate that the semantics-guided approach significantly outperforms the ORB SLAM2 framework in dynamic indoor environments, which is crucial for improving the robustness and reliability of the SLAM system.

This paper presents an indoor floor positioning method with the smartphone's barometer for the purpose of solving the problem of low availability and high environmental dependence of the traditional floor positioning technology. First, an initial floor position algorithm with the \"entering\" detection algorithm has been obtained. Second, the user's going upstairs or downstairs activities are identified by the characteristics of the air pressure fluctuation. Third, the moving distance in the vertical direction and the floor change during going upstairs or downstairs are estimated to obtain the accurate floor position. In order to solve the problem of the floor misjudgment from different mobile phone's barometers, this paper calculates the pressure data from the different cell phones, and effectively reduce the errors of the air pressure estimating the elevation which is caused by the heterogeneity of the mobile phones. The experiment results show that the average correct rate of the floor identification is more than 85% for three types of the cell phones while reducing environmental dependence and improving availability. Further, this paper compares and analyzes the three common floor location methods - the WLAN Floor Location (WFL) method based on the fingerprint, the Neural Network Floor Location (NFL) methods, and the Magnetic Floor Location (MFL) method with our method. The experiment results achieve 94.2% correct rate of the floor identification with Huawei mate10 Pro mobile phone.

In this research, a new Map/INS/Wi-Fi integrated system for indoor location-based service (LBS) applications based on a cascaded Particle/Kalman filter framework structure is proposed. Two-dimension indoor map information, together with measurements from an inertial measurement unit (IMU) and Received Signal Strength Indicator (RSSI) value, are integrated for estimating positioning information. The main challenge of this research is how to make effective use of various measurements that complement each other in order to obtain an accurate, continuous, and low-cost position solution without increasing the computational burden of the system. Therefore, to eliminate the cumulative drift caused by low-cost IMU sensor errors, the ubiquitous Wi-Fi signal and non-holonomic constraints are rationally used to correct the IMU-derived navigation solution through the extended Kalman Filter (EKF). Moreover, the map-aiding method and map-matching method are innovatively combined to constrain the primary Wi-Fi/IMU-derived position through an Auxiliary Value Particle Filter (AVPF). Different sources of information are incorporated through a cascaded structure EKF/AVPF filter algorithm. Indoor tests show that the proposed method can effectively reduce the accumulation of positioning errors of a stand-alone Inertial Navigation System (INS), and provide a stable, continuous and reliable indoor location service.

With the development of indoor location-based services (ILBS), the dual foot-mounted inertial navigation system (DF-INS) has been extensively used in many fields involving monitoring and direction-finding. It is a widespread ILBS implementation with considerable application potential in various areas such as firefighting and home care. However, the existing DF-INS is limited by a high inaccuracy rate due to the highly dynamic and non-stable stride length thresholds. The system also provides less clear and significant information visualization of a person’s position and the surrounding map. This study proposes a novel wearable-foot IOAM-inertial odometry and mapping to address the aforementioned issues. First, the person’s gait analysis is computed using the zero-velocity update (ZUPT) method with data fusion from ultrasound sensors placed on the inner side of the shoes. This study introduces a dynamic minimum centroid distance (MCD) algorithm to improve the existing extended Kalman filter (EKF) by limiting the stride length to a minimum range, significantly reducing the bias in data fusion. Then, a dual trajectory fusion (DTF) method is proposed to combine the left- and right-foot trajectories into a single center body of mass (CBoM) trajectory using ZUPT clustering and fusion weight computation. Next, ultrasound-type mapping is introduced to reconstruct the surrounding occupancy grid map (S-OGM) using the sphere projection method. The CBoM trajectory and S-OGM results were simultaneously visualized to provide comprehensive localization and mapping information. The results indicate a significant improvement with a lower root mean square error (RMSE = 1.2 m) than the existing methods.

Several Wireless Fidelity (WiFi) fingerprint datasets based on Received Signal Strength (RSS) have been shared for indoor localization. However, they can’t meet all the demands of WiFi RSS-based localization. A supplementary open dataset for WiFi indoor localization based on RSS, called as SODIndoorLoc, covering three buildings with multiple floors, is presented in this work. The dataset includes dense and uniformly distributed Reference Points (RPs) with the average distance between two adjacent RPs smaller than 1.2 m. Besides, the locations and channel information of pre-installed Access Points (APs) are summarized in the SODIndoorLoc. In addition, computer-aided design drawings of each floor are provided. The SODIndoorLoc supplies nine training and five testing sheets. Four standard machine learning algorithms and their variants (eight in total) are explored to evaluate positioning accuracy, and the best average positioning accuracy is about 2.3 m. Therefore, the SODIndoorLoc can be treated as a supplement to UJIIndoorLoc with a consistent format. The dataset can be used for clustering, classification, and regression to compare the performance of different indoor positioning applications based on WiFi RSS values, e.g., high-precision positioning, building, floor recognition, fine-grained scene identification, range model simulation, and rapid dataset construction.

Indoor location-based services (iLBS) are extremely dynamic and changeable, and include numerous resources and mobile devices. In particular, the network infrastructure requires support for high scalability in the indoor environment, and various resource lookups are requested concurrently and frequently from several locations based on the dynamic network environment. A traditional map-based centralized approach for iLBSs has several disadvantages: it requires global knowledge to maintain a complete geographic indoor map; the central server is a single point of failure; it can also cause low scalability and traffic congestion; and it is hard to adapt to a change of service area in real time. This paper proposes a self-organizing and fully distributed platform for iLBSs. The proposed self-organizing distributed platform provides a dynamic reconfiguration of locality accuracy and service coverage by expanding and contracting dynamically. In order to verify the suggested platform, scalability performance according to the number of inserted or deleted nodes composing the dynamic infrastructure was evaluated through a simulation similar to the real environment.