Indoor Location-based Services Research Articles

Towards Integration of IndoorGML and GDF for Robot Navigation in Warehouses

Abstract. With the development of the navigation technology, the outdoor navigation has made great progress, whereas the indoor navigation has some areas which is underdeveloped, insufficient to meet the rapidly increasing demands of people as well as the robotics. Even though, the advance in indoor navigation technology still has really brought a wide range of applications and a broad market, for instance, the flourishing intelligent warehouse system utilizes multi-robot operation which have the certain requirement for an accurate indoor navigation system. As for the indoor navigation, the OGC standard IndoorGML has been released and undergoing revision constantly. While the document really provides more advantageous support for the applications of Indoor Location-Based Services (LBS), in some aspects, especially the door-to-door navigation and the warehouse environment, it is not sufficiently adaptable, with still some room for improvement. IndoorGML is powerful for the common indoor scenarios like malls and offices, while as for carefully-arranged warehouse environment and other large-scale operation scenarios with multi-robots that is more similar to an ordered system, it is obviously insufficient. In this paper, we discuss about the potential to combination of IndoorGML and ITS standard ISO 20524 (GDF5.1), and extend the OGC standard indoorGML. We analyze the definition as well as function of related concepts, making some comparisons between these two standards. We conclude that these two standards are well-matched with vital potential to merge and unify the indoor and outdoor systems for spatial information.

Current Status and Future Trends of Meter-Level Indoor Positioning Technology: A Review

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.

Location-Based Services for Indoor Environments: A User Experience Test

The location-based services (LBS) user experience in controlled indoor settings in this scientific research. The environments included a range of user demographics, device configurations, and ambient factors. According to our findings, customer satisfaction ratings ranged from 5 to 9 on a scale of 1 to 10, and the average location accuracy was 1.1 meters (±0.4 meters) across various interactions and locales. These conclusions were supported by qualitative comments, which expressed satisfaction with the effectiveness of the check-in procedures and the practicality of several LBS features. Negative reviews, on the other hand, revealed difficulties with positioning technology accuracy, highlighting the intricate interactions among variables influencing indoor location-based services user experiences. This study provides a solid scientific basis for indoor location-based services (LBS) optimization, highlighting the need of tackling practical issues to improve user contentment and the usefulness of such services in indoor environments.

Conversational Localization

We propose a novel sensorless approach to indoor localization by leveraging natural language conversations with users, which we call conversational localization. To show the feasibility of conversational localization, we develop a proof-of-concept system that guides users to describe their surroundings in a chat and estimates their position based on the information they provide. We devised a modular architecture for our system with four modules. First, we construct an entity database with available image-based floor maps. Second, we enable the dynamic identification and scoring of information provided by users through our utterance processing module. Then, we implement a conversational agent that can intelligently strategize and guide the interaction to elicit localizationally valuable information from users. Finally, we employ visibility catchment area and line-of-sight heuristics to generate spatial estimates for the user's location. We conduct two user studies in designing and testing the system. We collect 800 natural language descriptions of unfamiliar indoor spaces in an online crowdsourcing study to learn the feasibility of extracting localizationally useful entities from user utterances. We then conduct a field study with 10 participants at 10 locations to evaluate the feasibility and performance of conversational localization. The results show that conversational localization can achieve within-10 meter localization accuracy at eight out of the ten study sites, showing the technique's utility for classes of indoor location-based services.

Geomagnetic indoor localisation-based dilated convolution neural networks

ABSTRACT With the rapid development of mobile Internet, the demand for Indoor Location-Based Service (ILBS) keeps rising. Geomagnetic field fingerprinting-based positioning scheme, with low cost, high accuracy and good stability, has gradually earned the attention of researchers. There are many problems in the existing indoor localisation researches based on geomagnetic fingerprint recognition, such as heavy workload of fingerprint collection, insufficient positioning accuracy, high walking cost of positioning and long positioning delay. Therefore, a localisation system named DCGIL is proposed in this thesis. In DCGIL, a segmentation length estimation algorithm of geomagnetic fingerprint sequence is proposed to evaluate the minimum segmentation length, the shortest walking distance. Then, through an overlapping fingerprint segmentation method, the gap between coordinate points and the cost of fingerprint matching are reduced. Further, dilated convolutional neural networks is utilised for segmented fingerprint classification, which could improve the accuracy of fingerprint classification compared with the existing classification methods. Finally, DCGIL utilises the sliding window to align and match the segmented fingerprint. Experiments show that the localisation accuracy reached 1.21 metres at 3.8 m shortest walking distance derived from segmentation length estimation algorithm, which improved 28% compared to existing algorithms.

AUTOMATIC GENERATION OF ROUTING GRAPHS FOR INDOOR-OUTDOOR TRANSITIONAL SPACE TO SUPPORT SEAMLESS NAVIGATION

Abstract. With the fast development of urbanization, the complexity of built environments has dramatically increased, driving a need for assistance in seamless indoor-outdoor navigation. This requires integration of spatial information of indoor and outdoor environments from heterogeneous data sources. While outdoor road network data is largely available from many sources (such as OpenStreetMap), indoor spatial information is either inexistent or is inconsistently represented using several different standards. Among these standards, IndoorGML is a well-developed standard with the focus on indoor location-based services. This standard has already been accepted by Open Geospatial Consortium (OGC) and is now under active development. Although in IndoorGML some mechanisms have been defined to enable integration of indoor and outdoor networks, there is still a lack of concrete guidelines for determination of indoor-outdoor connections. It also lacks solid scientific foundations and efficient tools to extract the connecting nodes and edges that link indoor and outdoor spaces. To address this gap, in this study we focus on the connection of indoor and outdoor spaces and aim to provide a tool, which can automatically construct navigation graphs of the indoor-outdoor transitional space to support seamless integration of indoor-outdoor navigation. To this end, voxel-based modeling approaches are used to model the connecting space between indoor and outdoor environments. Based on Python, we develop the intended tool, which can generate voxel models from point clouds, identify navigable space by taking into account the characteristics of agents (such as pedestrians, wheelchairs, and vehicles), and automatically build navigation graphs linking IndoorGML networks with outdoor street networks. It is expected that the methodology and tools developed from this project will benefit the IndoorGML ecosystem and greatly advance the capability of IndoorGML in representing navigable space to support location-based services.

Deep Neural Network-Based Fusion Localization Using Smartphones

Indoor location-based services (LBS) have tremendous practical and social value in intelligent life due to the pervasiveness of smartphones. The magnetic field-based localization method has been an interesting research hotspot because of its temporal stability, ubiquitousness, infrastructure-free nature, and good compatibility with smartphones. However, utilizing discrete magnetic signals may result in ambiguous localization features caused by random noise and similar magnetic signals in complex symmetric and large-scale indoor environments. To address this issue, we propose a deep neural network-based fusion indoor localization system that integrates magnetic and pedestrian dead reckoning (PDR). In this system, we first propose a ResNet-GRU-LSTM neural network model to achieve magnetic localization more accurately. Afterward, we put forward a multifeatured-driven step length estimation. A hierarchy GRU (H-GRU) neural network model is proposed, and a multidimensional dataset using acceleration and a gyroscope is constructed to extract more valid characteristics. Finally, more reliable and accurate pedestrian localization can be achieved under the particle filter framework. Experiments were conducted at two trial sites with two pedestrians and four smartphones. Results demonstrate that the proposed system achieves better accuracy and robustness than other traditional localization algorithms. Moreover, the proposed system exhibits good generality and practicality in real-time localization with low cost and low computational complexity.

SE-Loc: Security-Enhanced Indoor Localization With Semi-Supervised Deep Learning

Wireless indoor localization has become unavoidable for industrial indoor location-based services. Given the ubiquitous deployment of wireless access points (APs), WiFi fingerprinting of Received Signal Strength (RSS) has been widely adopted for indoor localization. Meanwhile, existing RSS fingerprint-based methods lack security-oriented considerations and are vulnerable to malicious attacks. When security vulnerabilities are exploited, mobile users may confront indoor localization mismatches, faults and even localization system failures. In this paper, we propose SE-Loc, a semi-supervised learning-based technique to enhance security and resiliency of fingerprint-based localization. The architecture of SE-Loc consists of two parts: (1) a correlation-based AP selection for processing RSS fingerprints and fingerprint-image generation, and (2) a deep learning model based on a denoising autoencoder and convolutional neural networks for robust feature learning and location matching. Extensive experiments show that under potential AP attacks, SE-Loc demonstrates superior performance on indoor localization over the state-of-the-art methods. With up to 100 malicious attacking APs in the UJIIndoorLoc edge server, SE-Loc can still achieve the lowest error fluctuation of 1.7 m and the highest average localization accuracy of 8.9 m.

Artificial Neural Network for Indoor Localization Based on Progressive Subdivided Quadrant Method

The exterior location of a user can be accurately determined using a global positioning system (GPS). However, accurately locating objects indoors poses challenges due to signal penetration limitations within buildings. In this study, an MLP with stochastic gradient descent (SGD) among artificial neural networks (ANNs) and signal strength indicator (RSSI) data received from a Zigbee sensor are used to estimate the indoor location of an object. Four fixed nodes (FNs) were placed at the corners of an unobstructed area measuring 3 m in both length and width. Within this designated space, mobile nodes (MNs) captured position data and received RSSI values from the nodes to establish a comprehensive database. To enhance the precision of our results, we used a data augmentation approach which effectively expanded the pool of selected cells. We also divided the area into sectors using an ANN to increase the estimation accuracy, focusing on selecting sectors that had measurements. To enhance both accuracy and computational speed in selecting coordinates, we used B-spline surface equations. This method, which is similar to using a lookup table, brought noticeable benefits: for indoor locations, the error margin decreased below the threshold of sensor hardware tolerance as the number of segmentation steps increased. By comparing our proposed deep learning methodology with the traditional fingerprinting technique that utilizes a progressive segmentation algorithm, we verified the accuracy and cost-effectiveness of our method. It is expected that this research will facilitate the development of practical indoor location-based services that can estimate accurate indoor locations with minimal data.

Indoor Scene Recognition Mechanism Based on Direction-Driven Convolutional Neural Networks

Indoor location-based services constitute an important part of our daily lives, providing position and direction information about people or objects in indoor spaces. These systems can be useful in security and monitoring applications that target specific areas such as rooms. Vision-based scene recognition is the task of accurately identifying a room category from a given image. Despite years of research in this field, scene recognition remains an open problem due to the different and complex places in the real world. Indoor environments are relatively complicated because of layout variability, object and decoration complexity, and multiscale and viewpoint changes. In this paper, we propose a room-level indoor localization system based on deep learning and built-in smartphone sensors combining visual information with smartphone magnetic heading. The user can be room-level localized while simply capturing an image with a smartphone. The presented indoor scene recognition system is based on direction-driven convolutional neural networks (CNNs) and therefore contains multiple CNNs, each tailored for a particular range of indoor orientations. We present particular weighted fusion strategies that improve system performance by properly combining the outputs from different CNN models. To meet users’ needs and overcome smartphone limitations, we propose a hybrid computing strategy based on mobile computation offloading compatible with the proposed system architecture. The implementation of the scene recognition system is split between the user’s smartphone and a server, which aids in meeting the computational requirements of CNNs. Several experimental analysis were conducted, including to assess performance and provide a stability analysis. The results obtained on a real dataset show the relevance of the proposed approach for localization, as well as the interest in model partitioning in hybrid mobile computation offloading. Our extensive evaluation demonstrates an increase in accuracy compared to traditional CNN scene recognition, indicating the effectiveness and robustness of our approach.

Automatic Generation of 3D Indoor Navigation Networks from Building Information Modeling Data Using Image Thinning

Navigation networks are a common form of indoor map that provide the basis for a wide range of indoor location-based services, intelligent tasks for indoor robots, and three-dimensional (3D) geographic information systems. The majority of current indoor navigation networks are manually modeled, resulting in a laborious and fallible process. Building Information Modeling (BIM) captures design information, allowing for the automated generation of indoor maps. Most existing BIM-based navigation systems for floor-level wayfinding rely on well-defined spatial semantics, and do not adapt well to buildings with irregular 3D shapes, which can make cross-floor path generation difficult. This research introduces an innovative approach to generating 3D indoor navigation networks automatically from BIM data using image thinning, which is referred to as GINIT. Firstly, GINIT extracts grid-based maps for floors from BIM data using only two types of semantics, i.e., slabs and doors. Secondly, GINIT captures cross-floor paths from building components by projecting 3D forms onto a 2D image, thinning the 2D image to capture the 2D projection path, and crossing over the 2D routes with 3D routes to restore the 3D path. Finally, to demonstrate the effectiveness of GINIT, experiments were conducted on three real-world multi-floor buildings, evaluating its performance across eight types of cross-layer architectural component. GINIT overcomes the dependency of space definitions in current BIM-based navigation network generation schemes by introducing image thinning. Due to the adaptability of navigation image thinning to any binary image, GINIT is capable of generating navigation networks from building components with diverse 3D shapes. Moreover, the current studies on indoor navigation network extraction mainly use geometry theory, while this study is the first to generate 3D indoor navigation networks automatically using image thinning theory. The results of this study will offer a unique perspective and foster the exploration of imaging theory applications of BIM.

Monocular Depth Estimation for 3D Map Construction at Underground Parking Structures

Converting the actual scenes into three-dimensional models has inevitably become one of the fundamental requirements in autonomous driving. At present, the main obstacle to large-scale deployment is the high-cost lidar for environment sensing. Monocular depth estimation aims to predict the scene depth and construct a 3D map via merely a monocular camera. In this paper, we add geometric consistency constraints to address the non-Lambertian surface problems in depth estimation. We also utilize the imaging principles and conversion rules to produce a 3D scene model from multiple images. We built a prototype and conduct extensive experiments in a corridor and an underground parking structure, and the results show the effectiveness for indoor location-based services.

Robust TDOA-Based Indoor Localization Using Improved Clock-Sync-Scheme and Multilevel Constrained ARPF

The need for indoor positioning research is driven by the contradiction between the lack of satisfactory location information available indoors and the emergence of an increasing number of indoor location-based services. The ability of ultra-wideband (UWB) technology to effectively estimate the signal arrival time and thereby obtain highly accurate ranging information makes it a research hotspot for indoor positioning systems. However, the scalability, user capacity, and ranging performance degradation under non-line-of-sight (NLOS) conditions are challenges that hinder its wide application. To address these issues, this paper proposes an improved highly available indoor positioning system that uses time difference of arrival (TDOA)-based UWB. Within this system, a novel UWB wireless clock synchronization scheme was designed to reduce synchronization errors, improve ranging accuracy, reduce synchronization frequency, and allocate more channel time for positioning. To identify observations with NLOS errors and mitigate their effects on localization, this paper implements an adaptive robust particle filtering algorithm (ARPF) based on multiple robustness factors. The multi-level constrained ARPF considers information such as signal quality, historical observations, and floor map. In a real-world environment, the proposed method is tested using multiple forms of tag devices. The results show that the proposed method outperforms the general particle filter, cutting the relative error by about the half, especially in the worst badge tests.

A survey on indoor positioning security and privacy

With rising demand for indoor location-based services (LBS) such as location-based marketing, mobile navigation, etc., there has been considerable interest in indoor positioning methods as well as their security and privacy. Current survey papers on indoor positioning methods mainly focus on positioning accuracy, whereas discussion on security and privacy considerations is limited. While there are survey papers on the security/privacy of LBS, they mainly focus on the services rather than the positioning methods. On the other hand, various survey papers on Internet of Things security/privacy mostly address device and system security. To fill the gap and complement the aforementioned survey papers, we conduct a systematic and comprehensive survey on indoor positioning security and privacy, focusing on the positioning methods. In particular, we provide the following contributions. First, based on general search (using the systematic PRISMA approach) and specific search, we study related papers published in recent years with the aim of addressing three research questions. Second, to facilitate the survey and study, we categorise the positioning methods into non-collaborative methods (i.e., proximity-based, geometric and fingerprinting methods), collaborative methods (i.e., mobile proximity-based and mobile geometric methods) and others (combining multiple technologies/methods). Third, for each method, we give an overview of the method and discuss its security and privacy issues. Last but not least, we highlight some future research directions and work on indoor positioning security and privacy. In particular, there is a need to conduct more research on collaborative positioning methods, including their security and privacy issues.

Indoor Positioning System Using a Single-Chip Millimeter Wave Radar

Estimation of indoor location is a key capability for indoor location-based services. Among various solutions, single-chip low-cost millimeter-wave (mmWave) radar as an emerging technology are getting more and more attention due to their inherent advantages such as penetration ability, low-power consumption. Currently, the vast majority of work is focused on hybrid positioning based on mmWave radar with other sensors, and there is a lack of research on single radar indoor positioning. In this article, we propose a novel indoor positioning system based on single-radar sensor, which determines the trajectory by estimating the velocity and heading of moving radar from sparse point clouds. Doppler velocity of detected object can be able to feedback the radar velocity, and for another hand, the proposed Cluster Center Association (CCA) method combined with normal distributions transform (NDT) can be used for heading estimation. In addition, we proposed the motion consistency constraints to enhance the robustness of the system. The experimental results show that the traditional NDT algorithm will fail due to the sparse point cloud and severe indoor multipath, and the proposed algorithm can provide the positioning accuracy within 1 m and the heading estimation error of 3°.

Developing a Method to Detect Indoor Spatial Entities in Omnidirectional Images for Constructing IndoorGML Data

The continuous increase in interest in indoor space demands the development of LBSs (Location Based Services) for navigation, routing, and space query in these environments. Applications providing these services must use visualization and topology data together to provide relevant services to users. Applications have frequently used geometric object-based visualization models for indoor space LBSs. These have the advantage of being easy to use with IndoorGML, an indoor topology model, but have the disadvantage of high construction cost and heavy data. Correspondingly, the image-based visualization model drew attention as an alternative model. However, using such models requires identifying objects on the image to be used alongside IndoorGML, presenting a limitation because it is difficult to identify objects in the image pixels directly. To overcome the limitations of image-based visualization models and reconsider the usability of indoor space LBS using images, this study presents a method to automatically detect spatial objects required to construct IndoorGML from images using deep learning. This methodology aims to detect objects mainly used in indoor LBSs, which include door and stair objects portrayed in indoor omnidirectional images. This study proposes a detailed method of constructing a training dataset for indoor spatial object detection. Moreover, this research presents a method of refining the training dataset and directly acquiring omnidirectional image data to train it as an object detection model that can be used universally in various buildings while maintaining compliant accuracy.

Smartphone-Based Indoor Floor Plan Construction via Acoustic Ranging and Inertial Tracking

The lack of indoor floor plans is one of the major obstacles to ubiquitous indoor location-based services. Dedicated mobile robots with high-precision sensors can measure and produce accurate indoor maps, but the deployment remains low for the public. Computer vision techniques are adopted by some existing smartphone-based methods to build the 3D point cloud, which have the cost of a quantity of the efforts of image collection and the risk of privacy issues. In this paper, we propose BatMapper-Plus which adopt acoustic ranging and inertial tracking to construct precise and complete indoor floor plans on smartphones. It emits acoustic signals to measure the distance from the smartphone to a neighbouring wall segment, and produces accessible areas by surrounding the building during walking. It also refines the constructed indoor floor plan to eliminate scattered segments, and identifies connection areas, including stairs and elevators among different floors. In addition, we propose an LSTM-based dead-reckoning model which is trained by outdoor IMU readings and GPS records, and use it to infer the step length during indoor walking, thereby improving the floor plan quality. We also elaborate how to use the constructed map for indoor navigation, i.e., a Dynamic Time Warping algorithm which automatically matches current inertial readings and historical sensory data during map construction to produce fine-grained walking guidance. To show our effectiveness compared with the state-of-the-art, we carry out extensive experiments in a teaching building and a residential building. It proves that our method is efficient without any privacy concerns and texture/illumination limitations.

GC-Loc

Indoor localization techniques play a fundamental role in empowering plenty of indoor location-based services (LBS) and exhibit great social and commercial values. The widespread fingerprint-based indoor localization methods usually suffer from the low feature discriminability with discrete signal fingerprint or high time overhead for continuous signal fingerprint collection. To address this, we introduce the collaboration mechanism and propose a graph attention based collaborative indoor localization framework, termed GC-Loc, which provides another perspective for efficient indoor localization. GC-Loc utilizes multiple discrete signal fingerprints collected by several users as input for collaborative localization. Specifically, we first construct an adaptive graph representation to efficiently model the relationships among the collaborative fingerprints. Then taking state-of-the-art GAT model as basic unit, we design a deep network with the residual structure and the hierarchical attention mechanism to extract and aggregate the features from the constructed graph for collaborative localization. Finally, we further employ ensemble learning mechanism in GC-Loc and devise a location refinement strategy based on model consensus for enhancing the robustness of GC-Loc. We have conducted extensive experiments in three different trial sites, and the experimental results demonstrate the superiority of GC-Loc, outperforming the comparison schemes by a wide margin (reducing the mean localization error by more than 42%).

Recent Advances in Pedestrian Inertial Navigation Based on Smartphone: A Review

Indoor location-based service is a hot research topic whose application market size is expected to grow from <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\$ $ </tex-math></inline-formula> 41 billion by 2022 and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\$ $ </tex-math></inline-formula> 58 billion by 2023. As a portable communication device, smartphones have inherent advantages (such as being an essential gadget in daily life, embedding various sensors, and having powerful computing and storage ability), which provides a great opportunity for human activity recognition and pedestrian navigation. Smartphone-based pedestrian inertial navigation systems (PINSs) utilize measurements from inertial sensors embedded in smartphones to reckon the pedestrian’s location. Compared with other indoor position technologies, PINS has the advantage of autonomy and continuity, and can achieve high-precision positioning in a short period. Though various PINS methods based on the smartphone have been proposed, up-to-date review papers that summarize relevant technologies, methods, and solutions of PINS are relatively less so far in the literature. This article aims to provide an elaborated, timely, and valuable survey of different infrastructure-free pedestrian positioning and navigation techniques, including pedestrian inertial navigation methods, performance evaluation, and applications. Finally, current challenges and future research trends are discussed. This work propels a better understanding of existing pedestrian inertial positioning methods. It is helpful for researchers to further design and develop more accurate and robust pedestrian positioning and navigation systems based on inertial sensors embedded in the smartphone.

Bluetooth Localization Technology: Principles, Applications, and Future Trends

The rapid development of the Bluetooth technology offers a possible solution for indoor localization scenarios. Compared with other indoor localization technologies, such as vision, light detection and ranging, ultrawide band, etc., Bluetooth has been characterized by low cost, easy deployment, low energy consumption, and potentially high localization accuracy, which enable itself to be a competitive technology in indoor location-based services, the Internet of Things, and many other fields. In this article, we first present a comprehensive survey of Bluetooth localization technology, including the measurements for localization, working principles, and method comparison. We highlight the learning-based methods and integrated localization methods. Then, we review the applications and existing commercial solutions, revealing the possible directions for the industrialization of Bluetooth localization. Finally, this article proposes several open issues of Bluetooth localization (e.g., multichannel difference, multipath, co-channel interference, and device heterogeneity) and projects several future trends.