High Accuracy Indoor Positioning Research Articles

An Up-View Visual-Based Indoor Positioning Method via Deep Learning

Indoor positioning plays a crucial role in various domains. It is employed in various applications, such as navigation, asset tracking, and location-based services (LBS), in Global Navigation Satellite System (GNSS) denied or degraded areas. The visual-based positioning technique is a promising solution for high-accuracy indoor positioning. However, most visual positioning research uses the side-view perspective, which is susceptible to interferences and may cause concerns about privacy and public security. Therefore, this paper innovatively proposes an up-view visual-based indoor positioning algorithm. It uses the up-view images to realize indoor positioning. Firstly, we utilize a well-trained YOLO V7 model to realize landmark detection and gross extraction. Then, we use edge detection operators to realize the precision landmark extraction, obtaining the landmark pixel size. The target position is calculated based on the landmark detection and extraction results and the pre-labeled landmark sequence via the Similar Triangle Principle. Additionally, we also propose an inertial navigation system (INS)-based landmark matching method to match the landmark within an up-view image with a landmark in the pre-labeled landmark sequence. This is necessary for kinematic indoor positioning. Finally, we conduct static and kinematic experiments to verify the feasibility and performance of the up-view-based indoor positioning method. The results demonstrate that the up-view visual-based positioning is prospective and worthy of research.

Integrated physical-layer secure visible light communication and positioning system based on polar codes

Visible light communication (VLC) with physical-layer security can provide information-theoretic security for the optical wireless channel based on the characteristics of the channel instead of encryption algorithms and secret keys at application layer. Since precise location information of communication parties is crucial for estimating channel states and designing secure communication schemes, this paper proposes an integrated visible light communication and positioning system which provides triple functionalities of high-accuracy indoor positioning, physical-layer secure visible light communication, and flicker mitigation illumination. A heterogeneous signal hybrid line coding scheme is proposed for the transmitter to converge the high-speed communication data signals and the low-speed positioning data signals, and a hybrid heterogeneous signal extraction scheme is proposed for the receiver to separate the hybrid heterogeneous signals with a high-bandwidth photodetector and a low-pass complementary metal-oxide-semiconductor (CMOS) image sensor. Based on the positioning information and the communication scheme, a polar codes-based forward error correction coding scheme is designed to achieve physical-layer security and transmission reliability simultaneously. Numerical results show that the proposed system can reach a secrecy code rate of 0.76 for a single-input single-output indoor VLC channel and a transmission efficiency of 0.38 without perceivable flicker. Experimental results show that the proposed system can achieve an average positioning accuracy of 3.35 cm and decrease the bit error rate of a legitimate receiver to a near error-free level (lower than 10−7) while keeping the bit error rate of an eavesdropper at 0.4887 (nearly 0.5) with a transmission data rate of 1 Mbps, resulting in near-zero suppression of the eavesdropped information and a high secrecy capacity of 0.9994.

Passive Location Estimation via Resonant Beam

High-accuracy indoor positioning is a demanding but challenging technology. In the Internet of Things applications, the existing indoor positioning schemes face the following challenges: 1) for the active positioning schemes, e.g., WiFi, Bluetooth, etc., the targets need power supply to emit signals; and 2) for the passive positioning schemes, e.g., Radar, Lidar, etc., the base station usually needs beam-steering control to aim at the targets for receiving sufficient reflective signals. Here, we propose a passive 3-dimensional monocular positioning scheme, resonant beam based positioning (RBP), which adopts a resonant beam for angle-of-arrival estimation and a time-of-flight module for distance estimation. We present the principle of the proposed scheme and establish analytical models and simulation tools for evaluating the impacting factors on the positioning accuracy including sensing noises, signal power, etc. To verify the RBP performance, we also present a testbed. Both numerical analysis and experimental measurements demonstrate that the positioning accuracy can be less than 1cm over 2m distance in <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$15^{\circ }$</tex-math></inline-formula> field of view. With RBP, high-accuracy and monocular without computation and high complexity positioning can be achieved while the target needs no power supply and the base station needs no beam-steering control. RBP is an important candidate for providing location-aware services in the Internet of everything and the proposed simulation models provide guidelines for system analysis and design.

Evaluation of a High-Accuracy Indoor-Positioning System with Wi-Fi Time of Flight (ToF) and Deep Learning

This article proposes the use of Wi-Fi ToF and a deep learning approach to build a cheap, practical, and highly-accurate IPS. To complement that, rather than using the classic geometrical approach (such as multilateration), it uses a more data-driven approach, i.e., the location fingerprinting technique. The fingerprint of a location, in this case, is a set of Wi-Fi ToFs between the target device and an access point (AP). Therefore, the number of APs in the area dictates the set size. The location fingerprinting technique requires a collection of fingerprints of various locations in the area to build a reference database or map. This database or map contains the information used to carry out the main task of the location fingerprinting technique, namely, estimating the position of a device based on its location fingerprint. For that task, we propose using a fully connected deep neural network (FCDNN) model to act as a positioning engine. The model is given a location fingerprint as its input to produce the estimated location coordinates as its output. We conduct an experiment to analyze the impact of the available AP pair in the dataset, from 1 unique AP pair, 2 AP pairs, and more, using WKNN and FCDNN to compare their performance. Our experimental results show that our IPS, DeepIndoor, can achieve an average positioning error or mean square error of 0.1749 m, and root mean square error of 0.5740 m in scenario 3, where 1–10 AP pairs or the raw dataset is used.

Using Data Pre-Processing and Convolutional Neural Network (CNN) to Mitigate Light Deficient Regions in Visible Light Positioning (VLP) Systems

New systems and technologies, such as Internet-of-Things (IOT) may require high reliability and high accuracy indoor positioning and tracking of persons and devices in indoor areas. Among different visible-light-positioning (VLP) schemes, received-signal-strength (RSS) scheme is relatively easy to implement. RSS VLP scheme can provide high accuracy positioning if the optical channels between the Txs and Rxs, as well as the received optical powers of different LEDs are accurately known. Unfortunately, these conditions are not easy to achieve in practice. Due to the limited field-of-view (FOV) of the LED lamps, light deficient regions will happen. This light deficient region could be large and significantly affect the positioning accuracy when performing 3-dimentional (3-D) VLP since at these light deficient regions, very weak or even no optical signal is received. In this work, we put forward and demonstrate the RSS VLP system utilizing data pre-processing and convolutional neural network (CNN) to mitigate light deficient regions in VLP system. Traditional ANN model and linear regression (LR) model are also compared with the CNN model, and the results illustrate that the proposed scheme outperforms the other schemes by not only improving the positioning accuracy, but also the error distribution uniformity.

Corrigendum to “High-Accuracy and Real-Time Indoor Positioning System Based on Visible Light Communication and Mobile Robot”

Corrigendum to “High-Accuracy and Real-Time Indoor Positioning System Based on Visible Light Communication and Mobile Robot”

A CSI-Based Indoor Positioning System Using Single UWB Ranging Correction.

A fingerprint-based localization system is an economic way to solve an indoor positioning problem. However, the traditional off-line fingerprint collection stage is a time-consuming and laborious process which limits the use of fingerprint-based localization systems. In this paper, based on ubiquitous Wireless Fidelity (Wi-Fi) equipment and a low-cost Ultra-Wideband (UWB) ranging system (with only one UWB anchor), a ready-to-use indoor localization system is proposed to realize long-term and high-accuracy indoor positioning. More specifically, in this system, it is divided into two stages: (1) an initial stage, and (2) a positioning stage. In the initial stage, an Inertial Measure Unit (IMU) is used to calculate the position using Pedestrian Dead Reckon (PDR) algorithm within a preset number of steps, and the location-related fingerprints are collected to train a Convolutional Neural Network (CNN) regression model; simultaneously, in order to make the UWB ranging system adapt to the Non-Line-of-Sight (NLoS) environment, the increments of acceleration and angular velocity in IMU and the increments of single UWB ranging measures are correlated to pre-train a Supported Vector Regression (SVR). After reaching the threshold of time or step number, the system is changed into a positioning stage, and the CNN predicts the position calibrated by corrected UWB ranging. At last, a series of practical experiments are conducted in the real environment; the experiment results show that, due to the corrected UWB ranging measures calibrating the CNN parameters in every positioning period, this system has stable localization results in a comparative long-term range. Additionally, it has the advantages of stability, low cost, anti-noise, etc.

A Novel NLOS Error Compensation Method Based IMU for UWB Indoor Positioning System

Electromagnetic wave is susceptible to the multipath effect, especially in complex indoor environment. As transmission of non-line of sight (NLOS), it leads to bigger measurement error, which makes positioning accuracy decline. In the paper, we aim to compensate the measurement error mainly affected by the NLOS to enhance the positioning accuracy. Conceptions of “virtual inertial point” and “environmental factor” are introduced, and moreover acceleration data of inertial measurement unit (IMU) are adopted. A novel and simple NLOS error compensation method for high accuracy indoor positioning system based UWB is proposed. Based on the principle of inertia, IMU can be used to fix motion trend of tag and to get coordinate of virtual inertial point. The measurement coordinate can be acquired by UWB system. We can calculate the distances between virtual inertial point and real measurement in same area, respectively under line of sight (LOS) and NLOS in advance. The ratio of two different distances can be defined as environmental factor, which is mainly used to reflect the influence of NLOS on positioning accuracy quantitatively relative to LOS in the paper. Environmental factor, as the weighted coefficient, is adopted to merge coordinates of the virtual inertial point and measurement to obtain the coordinate compensated under NLOS. It is a process of scaling measurement down to user-defined area for positioning accuracy requirement to reduce the impact of NLOS. At last, Kalman Filter is used to smooth the set of coordinate compensated under NLOS to further improve positioning accuracy. Experimental results show that our method can effectively compensate the NLOS error in the indoor positioning system based ultra-wide band (UWB) technology. The positioning accuracy is improved nearby 80% in NLOS area.

Kalman-Filter-Based Integration of IMU and UWB for High-Accuracy Indoor Positioning and Navigation

The emerging Internet of Things (IoT) applications, such as smart manufacturing and smart home, lead to a huge demand on the provisioning of low-cost and high-accuracy positioning and navigation solutions. Inertial measurement unit (IMU) can provide an accurate inertial navigation solution in a short time but its positioning error increases fast with time due to the cumulative error of accelerometer measurement. On the other hand, ultrawideband (UWB) positioning and navigation accuracy will be affected by the actual environment and may lead to uncertain jumps even under line-of-sight (LOS) conditions. Therefore, it is hard to use a standalone positioning and navigation system to achieve high accuracy in indoor environments. In this article, we propose an integrated indoor positioning system (IPS) combining IMU and UWB through the extended Kalman filter (EKF) and unscented Kalman filter (UKF) to improve the robustness and accuracy. We also discuss the relationship between the geometric distribution of the base stations (BSs) and the dilution of precision (DOP) to reasonably deploy the BSs. The simulation results show that the prior information provided by IMU can significantly suppress the observation error of UWB. It is also shown that the integrated positioning and navigation accuracy of IPS significantly improves that of the least squares (LSs) algorithm, which only depends on UWB measurements. Moreover, the proposed algorithm has high computational efficiency and can realize real-time computation on general embedded devices. In addition, two random motion approximation model algorithms are proposed and evaluated in the real environment. The experimental results show that the two algorithms can achieve certain robustness and continuous tracking ability in the actual IPS.

A High-Accuracy Indoor-Positioning Method with Automated RGB-D Image Database Construction

High-accuracy indoor positioning is a prerequisite to satisfy the increasing demands of position-based services in complex indoor scenes. Current indoor visual-positioning methods mainly include image retrieval-based methods, visual landmarks-based methods, and learning-based methods. To better overcome the limitations of traditional methods such as them being labor-intensive, of poor accuracy, and time-consuming, this paper proposes a novel indoor-positioning method with automated red, green, blue and depth (RGB-D) image database construction. First, strategies for automated database construction are developed to reduce the workload of manually selecting database images and ensure the requirements of high-accuracy indoor positioning. The database is automatically constructed according to the rules, which is more objective and improves the efficiency of the image-retrieval process. Second, by combining the automated database construction module, convolutional neural network (CNN)-based image-retrieval module, and strict geometric relations-based pose estimation module, we obtain a high-accuracy indoor-positioning system. Furthermore, in order to verify the proposed method, we conducted extensive experiments on the public indoor environment dataset. The detailed experimental results demonstrated the effectiveness and efficiency of our indoor-positioning method.

Single light-emitting diode-based high-accuracy indoor positioning system using symmetrical optical receiver

A visible light positioning (VLP) system based on a single light-emitting diode (LED) using a symmetrical receiver is presented. In the proposed receiver, a horizontal photodetector (PD) is located at the center and surrounded by four tilted PDs, which are arranged symmetrically to increase the accuracy in the actual VLP environments. Based on the received signal strength, the high-accuracy positioning can be easily achieved with only the LED’s location regardless of LED’s and PDs’ characteristics. In addition, the relationship between the positioning error and the PDs’ polar angles (α) is analyzed, which indicates that the polar angles changing from 20 deg to 30 deg are optimum. The experimental results show that when the polar angles are 20 deg, the positioning error changes from 0.79 to 3.97 cm and the average error is 2.32 cm. The proposed VLP system removes the intercell interference caused by multiple LEDs and can be easily applied in the LED-limited scenarios of low costs, low complexity, and high accuracy.

Multi-Phase Fingerprint Map Based on Interpolation for Indoor Localization Using iBeacons

It has traditionally been laborious and problematic to construct and maintain fingerprint maps for indoor positioning. In this paper, a multi-phase fingerprint map indoor localization method based on interpolation was proposed. The method addresses two needs: (1) to construct an efficient fingerprint map, and (2) to provide high-accuracy indoor positioning. For the effective collection of the real-time received signal strength indicator (RSSI), we proposed a mobile data-collection cart, equipped with a laser rangefinder and a smartphone, which can rapidly collect environmental data in indoor spaces. In practice, as some regions might be inaccessible, we adopted a kriging-based interpolation method that exploits the spatial autocorrelation of the RSSI to efficiently generate and update the fingerprint database. To overcome the instability of RSSI and improve positioning accuracy, we proposed a time-variant multi-phase fingerprint map, with specific fingerprint databases constructed for different time periods, thereby automatically employing the most appropriate fingerprint map according to the time period. Indoor experiments verified that the proposed method has practical value, with sufficient accuracy for general indoor applications. We believe that the proposed method provides a feasible and low-cost indoor positioning solution.

Proposal and Verification of High Accuracy Indoor Positioning Implemented Data Transmission Function using Sound by Spread Spectrum

Proposal and Verification of High Accuracy Indoor Positioning Implemented Data Transmission Function using Sound by Spread Spectrum

A high-accuracy indoor positioning system based on UWB

In this paper, a high-accuracy indoor positioning system based on the ultra-wideband (UWB) technique is proposed. The proposed system uses a simple ranging process to obtain the distance between the mobile node and the fixed base stations. Besides, an improved time of arrival (ToA) algorithm with Kalman filtering is proposed to improve the positioning accuracy. Measurements have been performed in the real indoor 13m*7.6m environment with many obstacles and the root-mean-square error (RMSE) is less than 0.3m. The proposed system offers a wide range of application in robotics, industrial automation, post-sorting system and so on.

INDOOR POSITIONING AND NAVIGATION BASED ON CONTROL SPHERECAL PANORAMIC IMAGES

Continuous indoor and outdoor positioning and navigation is the goal to achieve in the field of mobile mapping technology. However, accuracy of positioning and navigation will be largely degraded in indoor or occluded areas, due to receiving weak or less GNSS signals. Targeting the need of high accuracy indoor and outdoor positioning and navigation for mobile mapping applications, the objective of this study is to develop a novel method of indoor positioning and navigation with the use of spherical panoramic image (SPI). Two steps are planned in the technology roadmap. First, establishing a control SPI database that contains a good number of well-distributed control SPIs pre-acquired in the target space. A control SPI means an SPI with known exterior orientation parameters, which can be solved with a network bundle adjustment of SPIs. Having a control SPI database, the target space will be ready to provide the service of positioning and navigation. Secondly, the position and orientation of a newly taken SPI can be solved by using overlapped SPIs searched from the control SPI database. The method of matching SPIs and finding conjugate image features will be developed and tested. Two experiments will be planned and conducted in this paper to test the feasibility and validate the test results of the proposed methods. Analysis of appropriate number and distribution of needed control SPIs will also be included in the experiments with respect to different test cases.

INDOOR POSITIONING AND NAVIGATION BASED ON CONTROL SPHERECAL PANORAMIC IMAGES

Abstract. Continuous indoor and outdoor positioning and navigation is the goal to achieve in the field of mobile mapping technology. However, accuracy of positioning and navigation will be largely degraded in indoor or occluded areas, due to receiving weak or less GNSS signals. Targeting the need of high accuracy indoor and outdoor positioning and navigation for mobile mapping applications, the objective of this study is to develop a novel method of indoor positioning and navigation with the use of spherical panoramic image (SPI). Two steps are planned in the technology roadmap. First, establishing a control SPI database that contains a good number of well-distributed control SPIs pre-acquired in the target space. A control SPI means an SPI with known exterior orientation parameters, which can be solved with a network bundle adjustment of SPIs. Having a control SPI database, the target space will be ready to provide the service of positioning and navigation. Secondly, the position and orientation of a newly taken SPI can be solved by using overlapped SPIs searched from the control SPI database. The method of matching SPIs and finding conjugate image features will be developed and tested. Two experiments will be planned and conducted in this paper to test the feasibility and validate the test results of the proposed methods. Analysis of appropriate number and distribution of needed control SPIs will also be included in the experiments with respect to different test cases.

Improving IMES Localization Accuracy by Integrating Dead Reckoning Information.

Indoor positioning remains an open problem, because it is difficult to achieve satisfactory accuracy within an indoor environment using current radio-based localization technology. In this study, we investigate the use of Indoor Messaging System (IMES) radio for high-accuracy indoor positioning. A hybrid positioning method combining IMES radio strength information and pedestrian dead reckoning information is proposed in order to improve IMES localization accuracy. For understanding the carrier noise ratio versus distance relation for IMES radio, the signal propagation of IMES radio is modeled and identified. Then, trilateration and extended Kalman filtering methods using the radio propagation model are developed for position estimation. These methods are evaluated through robot localization and pedestrian localization experiments. The experimental results show that the proposed hybrid positioning method achieved average estimation errors of 217 and 1846 mm in robot localization and pedestrian localization, respectively. In addition, in order to examine the reason for the positioning accuracy of pedestrian localization being much lower than that of robot localization, the influence of the human body on the radio propagation is experimentally evaluated. The result suggests that the influence of the human body can be modeled.

スペクトラム拡散を用いた複数音源の収容可能な高精度屋内測位の提案と検証

スペクトラム拡散を用いた複数音源の収容可能な高精度屋内測位の提案と検証

High-accuracy indoor positioning system based on visible light communication

A visible light communication (VLC)-based high-accuracy indoor positioning system is proposed and demonstrated. In this system, the light-emitting diode identification (LED-ID) indicating the position information of the LED can be transmitted to the receiver by the illumination LED through VLC. In the meantime, with the aid of a camera and angular sensors of the mobile device, a coordinate transform can be employed to calculate the relative position between the receiver and the reference LED so that the position of the receiver can be determined. Finally, the experimental results show that 2-cm positioning accuracy can be achieved and the simulation results indicate that the positioning error can be limited within 4.7 cm when the accuracy of angular sensors is 1 deg.

WeC-4-3 A Cumulative Error Compensation Model of Dead Reckoning Toward A High Accuracy Indoor Positioning System

WeC-4-3 A Cumulative Error Compensation Model of Dead Reckoning Toward A High Accuracy Indoor Positioning System