This paper reviews the state of the art and future trends of indoor Positioning, Localization, and Navigation (PLAN). It covers the requirements, the main players, sensors, and techniques for indoor PLAN. Other than the navigation sensors such as Inertial Navigation System (INS) and Global Navigation Satellite System (GNSS), the environmental-perception sensors such as High-Definition map (HD map), Light Detection and Ranging (LiDAR), camera, the fifth generation of mobile network communication technology (5G), and Internet-of-Things (IoT) signals are becoming important aiding sensors for PLAN. The PLAN systems are expected to be more intelligent and robust under the emergence of more advanced sensors, multi-platform/multi-device/multi-sensor information fusion, self-learning systems, and the integration with artificial intelligence, 5G, IoT, and edge/fog computing.

/pdf/indoor-navigation-state-of-the-art-and-future-trends-2ozl1wnwm5.pdf

Indoor navigation: state of the art and future trends

The Internet of Things (IoT) has started to empower the future of many industrial and mass-market applications. Localization techniques are becoming key to add location context to IoT data without human perception and intervention. Meanwhile, the newly-emerged Low-Power Wide-Area Network (LPWAN) technologies have advantages such as long-range, low power consumption, low cost, massive connections, and the capability for communication in both indoor and outdoor areas. These features make LPWAN signals strong candidates for mass-market localization applications. However, there are various error sources that have limited localization performance by using such IoT signals. This paper reviews the IoT localization system through the following sequence: IoT localization system review -- localization data sources -- localization algorithms -- localization error sources and mitigation -- localization performance evaluation. Compared to the related surveys, this paper has a more comprehensive and state-of-the-art review on IoT localization methods, an original review on IoT localization error sources and mitigation, an original review on IoT localization performance evaluation, and a more comprehensive review of IoT localization applications, opportunities, and challenges. Thus, this survey provides comprehensive guidance for peers who are interested in enabling localization ability in the existing IoT systems, using IoT systems for localization, or integrating IoT signals with the existing localization sensors.

Location-Enabled IoT (LE-IoT): A Survey of Positioning Techniques, Error Sources, and Mitigation

The traditional unsupervised image segmentation methods are widely used in synthetic aperture radar (SAR) image segmentation due to the simple and convenient application process. In order to solve the time-consuming problem of the common methods, an SAR image segmentation method using region smoothing and label correction (RSLC) is proposed. In this algorithm, the image smoothing results are used to approximate the results of the spatial information polynomials of the image. Thus, the segmentation process can be realized quickly and effectively. Firstly, direction templates are used to detect the directions at different coordinates of the image, and smoothing templates are used to smooth the edge regions according to the directions. It achieves the smoothing of the edge regions and the retention of the edge information. Then the homogeneous regions are presented indirectly according to the difference of directions. The homogeneous regions are smoothed by using isotropic operators. Finally, the two regions are fused for K-means clustering. The majority voting algorithm is used to modify the clustering results, and the final segmentation results are obtained. Experimental results on simulated SAR images and real SAR images show that the proposed algorithm outperforms the other five state-of-the-art algorithms in segmentation speed and accuracy.

SAR Image Segmentation Using Region Smoothing and Label Correction

In this paper, we investigate how self-contained pedestrian navigation can be augmented by the use of foot-to-foot visual observations. The main contribution is a measurement model that uses Zero velocity UpdaTe (ZUPT) and relative position measurements between the two shoes obtained from shoe-mounted feature patterns and cameras. This measurement model provides directly the compensation measurements for the three position states and three velocity states of a pedestrian. The involved features for detection are independent of surrounding environments, thus, the proposed system has a constant computational complexity in any context. The performance of the proposed system was compared to a standalone ZUPT method and a relative-distance-aided ZUPT method. Simulation results showed an improvement in accumulated navigation errors by over 90%. Real-world experiments were conducted, exhibiting a maximum improvement of 85% in accumulated errors, verifying validity of the approach.

Pedestrian Inertial Navigation System Augmented by Vision-Based Foot-to-foot Relative Position Measurements

Because the characteristics of wireless propagation channels (especially indoor channels) are too diverse and complex, the distance estimation strategy of range-based positioning techniques should adaptively change depending on the environment. In this paper, we study unsupervised learning techniques that efficiently do this without human intervention. As users simply move around an area of interest with mobile devices, the proposed method autonomously learns the characteristics of the surrounding environments and changes the ranging strategy accordingly. To this end, we use either model-based or neural network (NN)-based ranging modules for estimating the distance from neighboring anchor nodes, calculate the position of the devices using trilateration techniques, and define cost functions that indirectly evaluate the accuracy of the ranging module based on the trilateration results. Moreover, by assigning a unique trainable variable to each device, the proposed method is also able to compensate for different characteristics between devices without ground truth data. The performance of the proposed method is verified with a real-time location tracking application using received signal strength (RSS) measurements from conventional Wi-Fi access points (APs) or round trip time (RTT) measurements from APs that support the fine timing measurement (FTM) protocol. In cases where a model-based ranging module is used, the proposed method closely achieves the benchmark performance, which perfectly optimizes all the trainable variables on the test data. If NNs are adopted in the ranging module, the proposed method even outperforms the benchmark and achieves an average positioning accuracy of up to 2.397 m using RSS measurements, and up to 1.547 m using RTT measurements under the 40 MHz bandwidth configuration.

/pdf/unsupervised-learning-techniques-for-trilateration-from-3lxebckuc4.pdf

Unsupervised Learning Techniques for Trilateration: From Theory to Android APP Implementation

Foot-mounted inertial navigation systems are suitable as the core pedestrian navigation system in GNSS denied environment for some applications (e.g., soldier and first responder positioning). However, to date the exiting zero velocity update-based algorithm still possesses some drawbacks including systematic heading drift. A beneficial strategy is to fuse the information of two foot-mounted systems to achieve more robust and reliable performance. In this paper, a novel equality constraint method based on pedestrian footstep mode is proposed to fuse the information of two systems in the absence of any additional ranging equipment. Real field experiments demonstrate that the proposed algorithm improves the performances of both two systems.

Data Fusion of Dual Foot-Mounted IMU for Pedestrian Navigation

A reliable and accurate position and orientation system (POS) is essential for indoor mobile mapping system (MMS). We propose a novel pedestrian POS solution for indoor MMS using backpack INS, foot mounted pedestrian dead reckoning (Foot-PDR) and sparse control points with known coordinates. The Foot-PDR can provide continuous high precision positioning with the corrections of the control points to maintain its absolute accuracy. Such precise positioning is integrated with the backpack INS to estimate the accurate position and orientation, similar to the GNSS/INS integration for the outdoor environment. The lever-arm variation issue between the backpack INS and the Foot-PDR is solved by catching a specific phase of the gait cycle that has consistent relative pose between the back and the heel of pedestrians. The feasibility of the proposed POS was verified by an outdoor large-scale open-sky area and an indoor office environment tests. The outdoor experimental results demonstrate that the estimated horizontal position and yaw angle has an accuracy of 0.07 m and 0.6°, respectively (when the control points are as sparse as 50 meters). The indoor experimental results demonstrate that the position accuracy is 0.07 m, 0.08 m, and 0.04 m, along the north, east, and height directions. Furthermore, the proposed POS does not require expensive infrastructure support and is immune to indoor environment changes.

A Novel Position and Orientation System for Pedestrian Indoor Mobile Mapping System

Accurate multispectral image segmentation is essential in remote sensing research. Traditional fuzzy clustering algorithms used to segment multispectral images have several disadvantages, including: (1) they usually only consider the pixels’ grayscale information and ignore the interaction between pixels; and, (2) they are sensitive to noise and outliers. To overcome these constraints, this study proposes a multispectral image segmentation algorithm based on fuzzy clustering combined with the Tsallis entropy and Gaussian mixture model. The algorithm uses the fuzzy Tsallis entropy as regularization item for fuzzy C-means (FCM) and improves dissimilarity measure using the negative logarithm of the Gaussian Mixture Model (GMM). The Hidden Markov Random Field (HMRF) is introduced to define prior probability of neighborhood relationship, which is used as weights of the Gaussian components. The Lagrange multiplier method is used to solve the segmentation model. To evaluate the proposed segmentation algorithm, simulated and real multispectral images were segmented using the proposed algorithm and two other algorithms for comparison (i.e., Tsallis Fuzzy C-means (TFCM), Kullback–Leibler Gaussian Fuzzy C-means (KLG-FCM)). The study found that the modified algorithm can accelerate the convergence speed, reduce the effect of noise and outliers, and accurately segment simulated images with small gray level differences with an overall accuracy of more than 98.2%. Therefore, the algorithm can be used as a feasible and effective alternative in multispectral image segmentation, particularly for those with small color differences.

https://www.mdpi.com/2072-4292/11/23/2772/pdf

Multispectral Image Segmentation Based on a Fuzzy Clustering Algorithm Combined with Tsallis Entropy and a Gaussian Mixture Model

The invention provides an indoor positioning fingerprint acquisition method. A first inertial navigation module and a second inertial navigation module are mounted at the feet of a target object, so that it is guaranteed that the first inertial navigation module and the feet, and the second inertial navigation module and the feet do not generate position offsets; then combined calculation is performed by utilizing zero-velocity correction and relative position relation constraints among multiple modules to obtain high-precision position information; an indoor positioning fingerprint and position information of the inertial navigation modules are simultaneously received through fingerprint acquisition equipment, and time synchronization is completed by taking the position information and arrival time of the positioning fingerprint as a reference; and the processed position information is combined with the collected indoor positioning fingerprint, so that the positioning precision of theinertial navigation modules is further improved, and then the acquisition of indoor positioning fingerprint data is completed. The high-precision and efficient indoor positioning fingerprint acquisition is realized.

Indoor positioning fingerprint acquisition method

The invention provides a method and a system for indoor movement measurement, positioning and attitude determination method and system based on foot inertial sensors. The method comprises the steps of: installing a first inertial sensor at the position of an operator's foot heel, installing a second inertial sensor at the position of the operator's back, and performing time synchronization; resolving the position, the speed and the attitude angle information of the first inertial sensor; extracting position information of the first inertial sensor at the middle moment of a static period according to the condition that the relative positions of the back and the foot of a pedestrian are unchanged at the middle moment of the static period in the process of upright walking; and combining the data of the inertial sensor acquired by the second inertial sensor, constructing a mathematical model of the pedestrian movement measurement positioning and attitude determination system, performing resolving to obtain the position, speed and attitude angle information of the back of the pedestrian, and providing accurate position and attitude angle information for the indoor pedestrian movement measurement system. The method and the system for indoor movement measurement, positioning and attitude determination method and system based on foot inertial sensors can achieve the indoor movement measurement positioning and attitude determination with high precision, high efficiency, low cost and simple operation.

Li Yu

Papers

Data Fusion of Dual Foot-Mounted IMU for Pedestrian Navigation

A Novel Position and Orientation System for Pedestrian Indoor Mobile Mapping System

Multispectral Image Segmentation Based on a Fuzzy Clustering Algorithm Combined with Tsallis Entropy and a Gaussian Mixture Model

Indoor positioning fingerprint acquisition method

Method and system for indoor movement measurement, positioning and attitude determination method and system based on foot inertial sensors