Indoor Navigation Applications Research Articles

Selecting Dynamic Path Planning Algorithm Based-Upon Ranking Approach for Omni-Wheeled Mobile Robot

Four Omni Wheels Mobile Robot (4OWMR) are widely utilized for indoor navigation applications such as autonomous transportation, surveillance, rescue, and search. This paper proposes a ranking approach to select the dynamic path planning algorithm. The selection process involves implementing the four path planning techniques (PRM, RRT, RRT*, Hybrid A*) on three different maps. The Weighted Sum Model (WSM) is then applied to each map to rank these algorithms based on several factors. Finally, the points of each algorithm in the three maps are added up, and the algorithm with the highest score is selected as the dynamic path planning algorithm. The results indicate that the Hybrid A* algorithm surpasses the others, scoring 46 points, which represents a 21.7% increase compared to PRM and RRT, and a 30.4% increase compared to RRT* when using the weighted sum ranking technique. Therefore, it can be selected as the dynamic path planning algorithm.

IMU and Bluetooth Data Fusion to Achieve Submeter Position Accuracy in Indoor Positioning

Indoor navigation applications have become widespread in recent years with the ability of mobile phones which determine the position. Due to the inefficiency of global positioning system (GPS) indoors, other positioning methods have been developed based on local networks using technologies such as Bluetooth, wireless networks, ultra-wideband signals, ultrasonic signals, and radio frequency identification modules. Various technologies yield high or medium accuracy. Combining data from multiple sources via fusion enhances location precision. In this study, indoor positions were estimated using trilateration with Bluetooth devices, and the accuracy was improved by applying filters to the data from inertial measurement unit (IMU) sensors on the phone. As a result of combining Bluetooth and IMU data with data fusion, submeter accuracy was achieved. The results obtained were tested at Yildiz Technical University-Istanbul Türkiye. It was determined that 92% of the data was obtained with submeter accuracy.

Augmented Reality Based Indoor Navigation System

Abstract: Every people, at some point in their life, may get lost inside a large auditorium, struggled to find their exit point at the airport, or may get late to a lecture because they could not find the right lecture hall at the university. This paper addresses the development of an Augmented Reality Based Indoor Navigation Application. It is based on four main modules, 3D model building, shortest path calculation, QR code scanning and path visualization. The application is developed in Unity using AR Core XR plugin management and AR Foundation plugin. ZXing library is used for scanning and decoding the QR codes. We aim at developing the front end in the simplest way possible so that the users can easily reach their destination by just opening the camera where the directions are shown as animations in their surroundings.

Muometric positioning system (muPS) utilizing direction vectors of cosmic-ray muons for wireless indoor navigation at a centimeter-level accuracy

Since the development of many future technologies are becoming more and more dependent on indoor navigation, various alternative navigation techniques have been proposed with radio waves, acoustic, and laser beam signals. In 2020, muometric positioning system (muPS) was proposed as a new indoor navigation technique; in 2022, the first prototype of wireless muPS was demonstrated in underground environments. However, in this first physical demonstration, its navigation accuracy was limited to 2–14 m which is far from the level required for the practical indoor navigation applications. This positioning error was an intrinsic problem associated with the clock that was used for determining the time of flight (ToF) of the muons, and it was practically impossible to attain cm-level accuracy with this initial approach. This paper introduces the completely new positioning concept for muPS, Vector muPS, which works by determining direction vectors of incoming muons instead of utilizing ToF. It is relatively easier to attain a 10-mrad level angular resolution with muon trackers that have been used for muographic imagery. Therefore, Vector muPS retains the unique capacity to operate wirelessly in indoor environments and also has the capacity to achieve a cm-level accuracy. By utilizing an essentially different concept from what is used in other navigation techniques, (measuring the distance between the reference and the receiver), Vector muPS enables more flexible, and longer-term stable positioning. Anticipated applications and the future outlook of Vector muPS is also discussed.

A Secure ZUPT-Aided Indoor Navigation System Using Blockchain in GNSS-Denied Environments.

This paper proposes a novel Blockchain-based indoor navigation system that combines a foot-mounted dual-inertial measurement unit (IMU) setup and a zero-velocity update (ZUPT) algorithm for secure and accurate indoor navigation in GNSS-denied environments. The system estimates the user's position and orientation by fusing the data from two IMUs using an extended Kalman filter (EKF). The ZUPT algorithm is employed to detect and correct the error introduced by sensor drift during zero-velocity intervals, thus enhancing the accuracy of the position estimate. The proposed Low SWaP-C blockchain-based decentralized architecture ensures the security and trustworthiness of the system by providing an immutable and distributed ledger to store and verify the sensor data and navigation solutions. The proposed system is suitable for various indoor navigation applications, including autonomous vehicles, robots, and human tracking. The experimental results provide clear and compelling evidence of the effectiveness of the proposed system in ensuring the integrity, privacy, and security of navigation data through the utilization of blockchain technology. The system exhibits an impressive ability to process more than 680 transactions per second within the Hyperledger-Fabric framework. Furthermore, it demonstrates exceptional accuracy and robustness, with a mean RMSE error of 1.2 m and a peak RMSE of 3.2 during a 20 min test. By eliminating the reliance on external signals or infrastructure, the system offers an innovative, practical, and secure solution for indoor navigation in environments where GNSS signals are unavailable.

Indoor Positioning on Smartphones Using Built-In Sensors and Visual Images

With the rapid development of mobile Internet technology, localization using visual image information has become a hot problem in the field of indoor localization research, which is not affected by signal multipath and fading and can achieve high accuracy localization in indoor areas with complex electromagnetic environments. However, in practical applications, position estimation using visual images is easily influenced by the user's photo pose. In this paper, we propose a multiple-sensor-assisted visual localization method in which the method constructs a machine learning classifier using multiple smart sensors for pedestrian pose estimation, which improves the retrieval efficiency and localization accuracy. The method mainly combines the advantages of visual image location estimation and pedestrian pose estimation based on multiple smart sensors and considers the effect of pedestrian photographing poses on location estimation. The built-in sensors of smartphones are used as the source of pedestrian pose estimation data, which constitutes a feasible location estimation method based on visual information. Experimental results show that the method proposed in this paper has good localization accuracy and robustness. In addition, the experimental scene in this paper is a common indoor scene and the experimental device is a common smartphone. Therefore, we believe that the proposed method in this paper has the potential to be widely used in future indoor navigation applications in complex scenarios (e.g., mall navigation).

Indoor Localization Using Positional Tracking Feature of Stereo Camera on Quadcopter

During the maneuvering of most unmanned aerial vehicles (UAVs), the GPS is one of the sensors used for navigation. However, this kind of sensor cannot handle indoor navigation applications well. Using a camera might be the answer to performing indoor navigation using its coordinate system. In this study, we considered indoor navigation applications using the ZED2 stereo camera for the quadcopter. To use the ZED 2 camera as a navigation sensor, we first transformed its coordinates into the North, East, down (NED) system to enable the drone to understand its position and maintain stability in a particular position. The experiment was performed using a real-time application to confirm the feasibility of this approach for indoor localization. In the real-time application, we commanded the quadcopter to follow triangular and rectangular paths. The results indicated that the quadcopter was able to follow the paths and maintain its stability in specific coordinate positions.

An indoor service area determination approach for pedestrian navigation path planning

ABSTRACT Indoor navigation has been studied for many years, but it still has many limitations. In current navigation, pedestrians need to tell navigation systems the destination, because it is one of the preconditions for path planning. However, in some indoor cases, pedestrians cannot specify a destination because they have no information about where it is or even cannot be sure if there is a desired one. We believe that determining a service area is a possible way to handle such cases. For example, a service area that can be reached within a 2-min walk. In this paper, we propose an indoor service area determination approach for pedestrian navigation path planning. We demonstrate this approach in a shopping mall with multi-floors. The results show that it can successfully compute the reachable spaces and thereby helping people to select and arrive at the most appropriate destination. This approach is also useful for other indoor navigation applications within public buildings like offices, airports, theaters, hospitals, and museums where pedestrians would like to make a choice between multiple facilities of the same type, such as printers, registration desks, ATMs, AED, garbage bins, even exits.

Different Path Planning Techniques for an Indoor Omni-Wheeled Mobile Robot: Experimental Implementation, Comparison and Optimization

Omni-wheeled mobile robots (Omni WMRs) are commonly used in indoor navigation applications like surveillance, search and rescue, and autonomous transportation. They are always characterized by their versatility, mobility and high payload. This paper presents the mechatronic design, low-level control and high-level control of an indoor 4 Omni-Wheeled Mobile Robot (4OWMR). Since autonomy and path planning are research necessities for WMRs, four heuristic and probabilistic path-planning techniques are chosen for experimental implementation. The selected techniques are PRM (Probabilistic Roadmaps), RRT (Rapidly exploring Random Tree), RRTSTAR (RRT*), and ASTAR (A*) algorithms. The proposed environments are static, expressed by maps with unknown nodes and obstacles. Local path planning is implemented with simultaneous localization and mapping (SLAM). Path planning techniques are programmed, and the obtained paths are optimized by a multi-objective genetic algorithm technique to ensure the shortest path and its smoothness. The optimized paths are deployed to the 4OWMR. The obtained results are compared in terms of travel time, travel distance, average velocity and convergence error. A ranking technique is utilized to rank the obtained results and show the most preferred technique in terms of energy consumption and convergence accuracy in addition to the overall ranking. Experimental results showed that the Hybrid A* algorithm produced the best-generated paths with respect to other techniques.

SEMANTIC SEGMENTATION OF INDOOR 3D POINT CLOUDS BY JOINT OPTIMIZATION OF GEOMETRIC FEATURES AND NEURAL NETWORKS

Abstract. Indoor navigation, indoor robotics, and other deep applications of interior space can be realized through semantic segmentation of 3D point clouds. We propose a semantic segmentation method for point clouds that uses geometric features of point clouds and neural networks to address the problem of incomplete and inconsistent segmentation objectives in existing semantic segmentation methods. Using neural networks, semantic labels are extracted from indoor structural information as the first step. The paper proposes a probabilistic model to cross-validate the initial segmentation results with the segmentation results of geometric features to achieve joint optimization of the results for semantic segmentation. Three sets of indoor point clouds data from simple to complex indoor scenes are used to test the accuracy and validity of the segmentation method proposed in this paper. The experimental results demonstrate that the method proposed in this paper can effectively improve the semantic segmentation accuracy of indoor 3D point clouds.

Indoor Navigation Application in Shopping Mall Based on Augmented Reality (AR)

The amount and frequency of shopping in department stores reduce due to COVID-19 outbreak all over the world. Thus, consumers have to give precedence to planning to go shopping in department stores in order to reduce social interaction time so as to reduce the risk of COVID-19 infection. This research introduced the development of an application prototype for product location search based on augmented reality (AR) for facilitating customers to search for desirable products with no need to ask salespersons and no need to go for product search on their own. Besides, the introduced application could also present statistical data related to behavior and interest in shopping. Organizations can use the data to make plans for decision making on selling, and to use it as a supportive tool to increase their competitiveness.

An Indoor Space Model of Building Considering Multi-Type Segmentation

Indoor space is a core part of supporting indoor applications. Most of the existing indoor space models are expressed from three space scales: building, floor, and room, and the granularity is not fine enough, lacking the expression of each functional subspace inside the room. In this study, we first analyzed the spatio-temporal segmentation characteristics of indoor space, and proposed a multi-level indoor space model framework that takes into account multiple types of segmentation. As well, based on the IFC (Industry Foundation Classes) standard, the extension of the indoor functional subspace was realized. The experimental results showed that the indoor space model proposed in this paper can effectively support the expression of functional subspace under the multi-type segmentation based on indoor elements, especially from the aspects of semantics, geometry, relationship, and attribute. This study enriches the granularity of existing indoor models and provides support for refined indoor navigation and evacuation applications.

MICRO AND MACRO QUADCOPTER DRONES FOR INDOOR MAPPING TO SUPPORT DISASTER MANAGEMENT

Abstract. The use of drones to explore indoor spaces has gained attention and popularity for disaster management and indoor navigation applications. In this paper we present the operations and mapping techniques of two drones that are different in terms of size, the sensors deployed, and the positioning and mapping techniques used. The first drone is a low-cost commercial quadcopter microdrone, a Crazyflie, while the second drone is a relatively expensive research quadcopter macrodrone, called MAX. We investigated their feasibility in mapping areas where satellite positioning is not available, such as indoor spaces. We compared the point clouds obtained by a multi-ranger deck, a multi-layer LIDAR scanner and a stereo camera, and assessed each against ground truth obtained with a terrestrial laser scanner. Results showed that both drones are capable of mapping relatively cluttered indoor environments and can provide point clouds that are sufficient for a quick exploration. Furthermore, the LIDAR scanner-based system can handle a relatively large office environment with an accumulated drift less than 0.02% (1 cm) on the Z-axis and 0.77% (50 cm) on the X and Y axes over a length trajectory of about 65 m. Despite the limited features of the sensor configuration of the Crazyflie, its performance is promising for mapping indoor spaces, given the relatively low deviation from the ground truth: cloud-to-cloud distances measured were generally less than 20 cm.

A UWB-Based Lighter-Than-Air Indoor Robot for User-Centered Interactive Applications.

Features such as safety and longer flight times render lighter-than-air robots strong candidates for indoor navigation applications involving people. However, the existing interactive mobility solutions using such robots lack the capability to follow a long-distance user in a relatively larger indoor space. At the same time, the tracking data delivered to these robots are sensitive to uncertainties in indoor environments such as varying intensities of light and electromagnetic field disturbances. Regarding the above shortcomings, we proposed an ultra-wideband (UWB)-based lighter-than-air indoor robot for user-centered interactive applications. We developed the data processing scheme over a robot operating system (ROS) framework to accommodate the robot’s integration needs for a user-centered interactive application. In order to explore the user interaction with the robot at a long-distance, the dual interactions (i.e., user footprint following and user intention recognition) were proposed by equipping the user with a hand-held UWB sensor. Finally, experiments were conducted inside a professional arena to validate the robot’s pose tracking in which 3D positioning was compared with the 3D laser sensor, and to reveal the applicability of the user-centered autonomous following of the robot according to the dual interactions.

The Integration of LADM and IndoorGML to Support the Indoor Navigation Based on the User Access Rights

Indoor navigation applications are actively investigated and developed due to their capacity to provide users with essential information in the modern extensive building complexes. Therefore, many researchers have developed a range of indoor navigation applications, which have focused on aspects such as localization, indoor route computation, and human spatial cognition. Unfortunately, current indoor navigation systems do not consider the user's access rights when it comes to navigating safely and effectively. This thesis delivers several contributions, which are based on international standards, to provide Indoor navigation aware of the user’s access rights. The thesis proposes: 1) a combined model of ISO’s LADM and OGC’s IndoorGML; 2) an enhancement of the UML class diagram of the conceptual model of IndoorGML; 3) a 2D LADM country profile of the Saudi Arabia; 4) a 3D LADM country profile of Saudi Arabia; 5) a conversion of the combined LADM and IndoorGML conceptual model to a technical model; 6) definitions of access rights for users of indoor environments during crisis based on the integrated model of LADM and IndoorGML; 7) a 3D web-based prototype application for indoor navigation making use of user access rights. The developed concepts and implementation have been acknowledged by the standardization organization ISO and OGC and considered for amending IndoorGML and LADM.

Developing a simulator of a mobile indoor navigation application as a tool for cartographic research

Abstract Solutions designed for indoor navigation are extremely rare compared to outdoor navigation; however, the potential for development is, therefore, very high. Several pilot projects exist in airports, universities, hospitals, and shopping centres. The difficulties in development are currently mainly due to the continuing low quality of indoor positioning and lack of widespread access to high-quality building models. A strong methodological basis for how the interior and exterior of buildings can be cartographically represented in navigation applications has also not yet been developed. Therefore, an attempt was made to design a virtual environment dedicated to supporting the design of indoor navigation applications. Authors present the results of a study aimed at creating a concept of a simulation environment accompanied by the assessment and preliminary validation of its technological feasibility in terms of the method and technology used – although it does not yet constitute a target study. The result was a fully functional prototype of a virtual test environment, which was successfully used in a pilot study on the effectiveness of different types of navigation guidance. The participants’ behaviour within the desktop virtual environment was investigated and their opinions were collected through a questionnaire. This research proved the technological feasibility of the proposed concept and demonstrated the usefulness of the Unreal Engine game engine in building new tools to support the work of cartographers. The created environment will be further developed and used in indoor mapping research.

A Universal LiDAR SLAM Accelerator System on Low-Cost FPGA

LiDAR (Light Detection and Ranging) SLAM (Simultaneous Localization and Mapping) serves as a basis for indoor cleaning, navigation, and many other useful applications in both industry and household. From a series of LiDAR scans, it constructs an accurate, globally consistent model of the environment and estimates a robot position inside it. SLAM is inherently computationally intensive; it is a challenging problem to realize a fast and reliable SLAM system on mobile robots with a limited processing capability. To overcome such hurdles, in this paper, we propose a universal, low-power, and resource-efficient accelerator design for 2D LiDAR SLAM targeting resource-limited FPGAs. As scan matching is at the heart of SLAM, the proposed accelerator consists of dedicated scan matching cores on the programmable logic part, and provides software interfaces to facilitate the use. Our accelerator can be integrated to various SLAM methods including the ROS (Robot Operating System)-based ones, and users can switch to a different method without modifying and re-synthesizing the logic part. We integrate the accelerator into three widely-used methods, i.e., scan matching, particle filter, and graph-based SLAM. We evaluate the design in terms of resource utilization, speed, and quality of output results using real-world datasets. Experiment results on a Pynq-Z2 board demonstrate that our design accelerates scan matching and loop-closure detection tasks by up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$14.84\times$ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$18.92\times$ </tex-math></inline-formula> , yielding <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$4.67\times$ </tex-math></inline-formula> , <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$4.00\times$ </tex-math></inline-formula> , and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$4.06\times$ </tex-math></inline-formula> overall performance improvement in the above methods, respectively. Our design enables the real-time performance while consuming only 2.4W and maintaining accuracy, which is comparable to the software counterparts and even the state-of-the-art methods.

An Efficient and Low-Latency Deep Inertial Odometer for Smartphone Positioning

The Global Positioning System (GPS) is not an effective solution for pedestrian indoor navigation using embedded devices because of poor signal penetration and high power requirements. Indoor positioning based purely on commercial-grade inertial measurement units (IMUs) may provide a good alternative but their significant noise and random bias must be eliminated. Although Kalman filters and pedestrian dead reckoning helped reduce the errors using IMUs, these methods cannot prevent the divergence of estimated position error. Deep learning was proposed for accurate position estimation with IMUs. Despite the progress, robust position estimation in mobile embedded devices using deep learning has not been established yet because of memory requirements, latency and inaccurate position estimation especially for stationary pedestrians. In this study, we present an efficient embedded deep learning approach for robust, real-time and accurate pedestrian position estimation using commercial Android smartphone IMUs. We first extended a publicly available deep inertial navigation dataset (OxIOD) with stationary data to enhance the positioning accuracy for both steady state and motion. Next, we trained and tested a deep learning architecture that yields a higher positioning accuracy with 50% lower network latency and 31% lower network size compared with earlier deep positioning networks such as IONet. Our real-time tests of the model in an Android smartphone indicated that the extension for the dataset reduces the position shift when the smartphone is stationary. Since our embedded deep learning solution simultaneously decreases the positioning error, latency and memory requirements, the solution paves the way for numerous practical indoor navigation applications.

An accurate cloud‐based indoor localization system with low latency

Indoor positioning systems are becoming increasing popular recently. While most existing indoor positioning studies focus on improving accuracy, less attention is paid to the latency problem. The traditional fusion algorithm uses the received signal strength-based (RSS-Based) positioning result to correct the current position of the system. However, it fails to keep up with the actual moving speed of the user during the navigation. The location retrospective adjustment (LRA) method proposed in this paper uses the RSS-Based positioning result to correct the past position of the system, which can effectively eliminate positioning delay and improve the real-time response of navigation. We tested in a 70 m linear promenade and found that the addition of LRA results in a reduction of the positioning error around −0.3 to +0.4 m, which improves 85%. Additionally, the LRA method alleviates the requirements for the immediate response of RSS positioning, and the RSS positioning algorithm can be moved to the cloud. It reduces the download resources and computing load on the mobile phone. The complete indoor navigation application is presented in HTML5 which allows users to navigate without having to download the APP in advance, and it takes only 4–9 s for users to launch the application for the first time. We tested the application in a hospital with a total floor area of 79,000 m2 in 7 buildings. The system achieves an average positioning accuracy of 0.65 m at a long navigation distance of 220 m. To our knowledge, this paper is the first to consider the latency issue in indoor navigation. The proposed LRA approach improves real-time navigation performance, lightens the computation load on the mobile phone, and allows cloud-based positioning systems to provide stable and accurate navigation even under poor network quality in crowded areas.

An Indoor Navigation Support for the Student Halls of Residence using Augmented Reality: A Design Perspective

Augmented Reality (AR) technology has become increasingly popular due to its potential use in an indoor environment. AR technology enables virtual information, such as navigation instructions, to be merged into the actual environment via a mobile screen. Using an AR-based Indoor Navigation speeds uptime while also being interactive in searching for a particular building location. Every year when new semester students enrol in the university, some students will have difficulty finding a particular location on the campus. The most searched for building upon arrival at the university is the student halls of residence. While searching for it, students waste time asking others for information or looking for a nearby campus map. Therefore, this project investigates the requirements needed for an AR-based indoor navigation application to be applied within the student halls of residence and identifies technical issues through a small-scale prototype development within a small navigational area. Seventy-one students participated in the feasibility study by responding to a set of questionnaires related to the Student Residence AR indoor navigation application. At the same time, four users with and without previous experience with AR applications evaluated the prototype application. The results identified that the more the students have difficulty searching, the more they require additional time to reach their destination and seek help from others, an excellent reason to implement the Student Residence AR indoor navigation. In addition, the prototype evaluation results discussed issues related to arrow path confusion, distance accuracy, assistive guideline, and software development challenges in AR development that could be beneficial to future developers and researchers.