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Multi‐Sensors for Indoor Localization and Tracking

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Navigation and Positioning".

Deadline for manuscript submissions: closed (15 March 2024) | Viewed by 18255

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Dr. Ran Liu

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Guest Editor

Engineering Product Development Pillar, Singapore University of Technology and Design, Singapore 487372, Singapore
Interests: indoor localization; SLAM; robotics; distributed computing; sensor fusion
Special Issues, Collections and Topics in MDPI journals

Dr. Pik Lik Billy Lau

E-Mail Website
Guest Editor

Engineering Product Development, Singapore University of Technology and Design, Singapore 487372, Singapore
Interests: data fusion; multi-robot systems; applied machine learning; smart city
Special Issues, Collections and Topics in MDPI journals

Dr. Jianwen Huo

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Guest Editor

Robot Technology Used for Special Environment Key Laboratory of Sichuan Province, Southwest University of Science and Technology, Mianyang 621010, China
Interests: multi-robot coordination; navigation and localization; sensor fusion
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

There is an increasing interest in indoor positioning due to the rapid growth of location-aware applications, such as city recommending systems and robot navigation. Although GPS is widely used in outdoor environments, it cannot be applied in indoor scenarios as satellite signals are easily reflected and diffracted by city buildings. A large number of researchers are focusing on localization in GPS-denied environments with various sensors (for example, radio frequency, visual, LiDAR, IMU, and acoustic). Localization in a given infrastructure (i.e., known map or distribution of anchors) has been widely studied. These approaches either require deploying a number of dedicated devices or need a tedious phase to collect radio fingerprints as a representation of a map of the environment. To address this problem, researchers have developed a solution called simultaneous localization and mapping (SLAM), which allows simultaneously localizing a mobile device and generating a map of the unknown environment.

This Special Issue will address localization and tracking leveraging the power of sensor fusion based on recent techniques. This includes positioning, tracking, indoor mapping, and location-aware applications.

Topics of interests include but are not limited to the following:

Simultaneous localization and mapping in robotics;
Emerging sensors for indoor localization and tracking;
Localization algorithms and implementations;
Sensor fusion techniques on localization and tracking;
Machine learning for indoor localization;
Localization and tracking for the Internet of Things;
Location-based services and applications.

Dr. Ran Liu
Dr. Pik Lik Billy Lau
Dr. Jianwen Huo
Guest Editors

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Keywords

indoor localization and tracking
radio-based localization
sensor fusion
dead reckoning
acoustic sensors
Internet of Things
smart city
SLAM

Related Special Issue

Multi‐sensors for Indoor Localization and Tracking: 2nd Edition in Sensors

Published Papers (9 papers)

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Research

16 pages, 11436 KiB

Open AccessArticle

Graph Trilateration for Indoor Localization in Sparsely Distributed Edge Computing Devices in Complex Environments Using Bluetooth Technology

by Yashar Kiarashi, Soheil Saghafi, Barun Das, Chaitra Hegde, Venkata Siva Krishna Madala, ArjunSinh Nakum, Ratan Singh, Robert Tweedy, Matthew Doiron, Amy D. Rodriguez, Allan I. Levey, Gari D. Clifford and Hyeokhyen Kwon

Sensors 2023, 23(23), 9517; https://doi.org/10.3390/s23239517 - 30 Nov 2023

Cited by 2 | Viewed by 1224

Abstract

Spatial navigation patterns in indoor space usage can reveal important cues about the cognitive health of participants. In this work, we present a low-cost, scalable, open-source edge computing system using Bluetooth low energy (BLE) beacons for tracking indoor movements in a large, 1700 [...] Read more.

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facility used to carry out therapeutic activities for participants with mild cognitive impairment (MCI). The facility is instrumented with 39 edge computing systems, along with an on-premise fog server. The participants carry a BLE beacon, in which BLE signals are received and analyzed by the edge computing systems. Edge computing systems are sparsely distributed in the wide, complex indoor space, challenging the standard trilateration technique for localizing subjects, which assumes a dense installation of BLE beacons. We propose a graph trilateration approach that considers the temporal density of hits from the BLE beacon to surrounding edge devices to handle the inconsistent coverage of edge devices. This proposed method helps us tackle the varying signal strength, which leads to intermittent detection of beacons. The proposed method can pinpoint the positions of multiple participants with an average error of 4.4 m and over 85% accuracy in region-level localization across the entire study area. Our experimental results, evaluated in a clinical environment, suggest that an ordinary medical facility can be transformed into a smart space that enables automatic assessment of individuals’ movements, which may reflect health status or response to treatment. Full article

(This article belongs to the Special Issue Multi‐Sensors for Indoor Localization and Tracking)

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18 pages, 46100 KiB

Open AccessArticle

MoTI: A Multi-Stage Algorithm for Moving Object Identification in SLAM

by Changqing Hu, Manlu Liu, Su Zhang, Yu Xie and Liguo Tan

Sensors 2023, 23(18), 7911; https://doi.org/10.3390/s23187911 - 15 Sep 2023

Cited by 2 | Viewed by 1172

Abstract

Simultaneous localization and mapping (SLAM) algorithms are widely applied in fields such as autonomous driving and target tracking. However, the effect of moving objects on localization and mapping remains a challenge in natural dynamic scenarios. To overcome this challenge, this paper proposes an algorithm for dynamic point cloud detection that fuses laser and visual identification data, the multi-stage moving object identification algorithm (MoTI). The MoTI algorithm consists of two stages: rough processing and precise processing. In the rough processing stage, a statistical method is employed to preliminarily detect dynamic points based on the range image error of the point cloud. In the precise processing stage, the radius search strategy is used to statistically test the nearest neighbor points. Next, visual identification information and point cloud registration results are fused using a method of statistics and information weighting to construct a probability model for identifying whether a point cloud cluster originates from a moving object. The algorithm is integrated into the front-end of the LOAM system, which significantly improves the localization accuracy. The MoTI algorithm is evaluated on an actual indoor dynamic environment and several KITTI datasets, and the results demonstrate its ability to accurately detect dynamic targets in the background and improve the localization accuracy of the robot. Full article

(This article belongs to the Special Issue Multi‐Sensors for Indoor Localization and Tracking)

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18 pages, 506 KiB

Open AccessArticle

Sequential Fusion Filter for State Estimation of Nonlinear Multi-Sensor Systems with Cross-Correlated Noise and Packet Dropout Compensation

by Liguo Tan, Yibo Wang, Changqing Hu, Xinbin Zhang, Liyi Li and Haoxiang Su

Sensors 2023, 23(10), 4687; https://doi.org/10.3390/s23104687 - 12 May 2023

Viewed by 1321

Abstract

This paper is concerned with the problem of state estimation for nonlinear multi-sensor systems with cross-correlated noise and packet loss compensation. In this case, the cross-correlated noise is modeled by the synchronous correlation of the observation noise of each sensor, and the observation noise of each sensor is correlated with the process noise at the previous moment. Meanwhile, in the process of state estimation, since the measurement data may be transmitted in an unreliable network, data packet dropout will inevitably occur, leading to a reduction in estimation accuracy. To address this undesirable situation, this paper proposes a state estimation method for nonlinear multi-sensor systems with cross-correlated noise and packet dropout compensation based on a sequential fusion framework. Firstly, a prediction compensation mechanism and a strategy based on observation noise estimation are used to update the measurement data while avoiding the noise decorrelation step. Secondly, a design step for a sequential fusion state estimation filter is derived based on an innovation analysis method. Then, a numerical implementation of the sequential fusion state estimator is given based on the third-degree spherical-radial cubature rule. Finally, the univariate nonstationary growth model (UNGM) is combined with simulation to verify the effectiveness and feasibility of the proposed algorithm. Full article

(This article belongs to the Special Issue Multi‐Sensors for Indoor Localization and Tracking)

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19 pages, 3344 KiB

Open AccessArticle

extendGAN+: Transferable Data Augmentation Framework Using WGAN-GP for Data-Driven Indoor Localisation Model

by Seanglidet Yean, Wayne Goh, Bu-Sung Lee and Hong Lye Oh

Sensors 2023, 23(9), 4402; https://doi.org/10.3390/s23094402 - 30 Apr 2023

Viewed by 1578

Abstract

For indoor localisation, a challenge in data-driven localisation is to ensure sufficient data to train the prediction model to produce a good accuracy. However, for WiFi-based data collection, human effort is still required to capture a large amount of data as the representation Received Signal Strength (RSS) could easily be affected by obstacles and other factors. In this paper, we propose an extendGAN+ pipeline that leverages up-sampling with the Dirichlet distribution to improve location prediction accuracy with small sample sizes, applies transferred WGAN-GP for synthetic data generation, and ensures data quality with a filtering module. The results highlight the effectiveness of the proposed data augmentation method not only by localisation performance but also showcase the variety of RSS patterns it could produce. Benchmarking against the baseline methods such as fingerprint, random forest, and its base dataset with localisation models, extendGAN+ shows improvements of up to 23.47%, 25.35%, and 18.88% respectively. Furthermore, compared to existing GAN+ methods, it reduces training time by a factor of four due to transfer learning and improves performance by 10.13%. Full article

(This article belongs to the Special Issue Multi‐Sensors for Indoor Localization and Tracking)

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18 pages, 2952 KiB

Open AccessArticle

A Hybrid Indoor Altimetry Based on Barometer and UWB

by Minghao Si, Yunjia Wang, Ning Zhou, Cheekiat Seow and Harun Siljak

Sensors 2023, 23(9), 4180; https://doi.org/10.3390/s23094180 - 22 Apr 2023

Cited by 4 | Viewed by 1446

Abstract

Accurate altimetry is essential for location-based services in commercial and industrial applications. However, current altimetry methods only provide low-accuracy measurements, particularly in multistorey buildings with irregular structures, such as hollow areas found in various industrial and commercial sites. This paper innovatively proposes a tightly coupled indoor altimetry system that utilizes floor identification to improve height measurement accuracy. The system includes two optimized algorithms that improve floor identification accuracy through activity detection and address the problem of difficult convergence of z-axis coordinates due to indoor coplanarity by applying constraints to iterative least squares (ILS). Two experiments were conducted in a teaching building and a laboratory, including an irregular environment with a hollow area. The results show that our proposed method for identifying floors based on activity detection outperforms other methods. In dynamic experiments, our method effectively eliminates repeated transformations during the up- and downstairs process, and in static experiments, it minimizes the impact of barometric drift. Furthermore, our proposed altimetry method based on constrained ILS achieves significantly improved positioning accuracy compared to ILS, 1D-CNN, and WC. Specifically, in the teaching building, our method achieves improvements of 0.84 m, 0.288 m, and 0.248 m, respectively, while in the laboratory, the improvements are 2.607 m, 0.696 m, and 0.625 m. Full article

(This article belongs to the Special Issue Multi‐Sensors for Indoor Localization and Tracking)

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16 pages, 8508 KiB

Open AccessArticle

Convolutional Model with a Time Series Feature Based on RSSI Analysis with the Markov Transition Field for Enhancement of Location Recognition

by Hyunji Lee and Jaeho Lee

Sensors 2023, 23(7), 3453; https://doi.org/10.3390/s23073453 - 25 Mar 2023

Cited by 6 | Viewed by 1839

Abstract

Although numerous schemes, including learning-based approaches, have attempted to determine a solution for location recognition in indoor environments using RSSI, they suffer from the severe instability of RSSI. Compared with the solutions obtained by recurrent-approached neural networks, various state-of-the-art solutions have been obtained using the convolutional neural network (CNN) approach based on feature extraction considering indoor conditions. Complying with such a stream, this study presents the image transformation scheme for the reasonable outcomes in CNN, obtained from practical RSSI with artificial Gaussian noise injection. Additionally, it presents an appropriate learning model with consideration of the characteristics of time series data. For the evaluation, a testbed is constructed, the practical raw RSSI is applied after the learning process, and the performance is evaluated with results of about 46.2% enhancement compared to the method employing only CNN. Full article

(This article belongs to the Special Issue Multi‐Sensors for Indoor Localization and Tracking)

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16 pages, 4340 KiB

Open AccessArticle

Water Surface Targets Detection Based on the Fusion of Vision and LiDAR

by Lin Wang, Yufeng Xiao, Baorui Zhang, Ran Liu and Bin Zhao

Sensors 2023, 23(4), 1768; https://doi.org/10.3390/s23041768 - 4 Feb 2023

Cited by 1 | Viewed by 2748

Abstract

The use of vision for the recognition of water targets is easily influenced by reflections and ripples, resulting in misidentification. This paper proposed a detection method based on the fusion of 3D point clouds and visual information to detect and locate water surface targets. The point clouds help to reduce the impact of ripples and reflections, and the recognition accuracy is enhanced by visual information. This method consists of three steps: Firstly, the water surface target is detected using the CornerNet-Lite network, and then the candidate target box and camera detection confidence are determined. Secondly, the 3D point cloud is projected onto the two-dimensional pixel plane, and the confidence of LiDAR detection is calculated based on the ratio between the projected area of the point clouds and the pixel area of the bounding box. The target confidence is calculated with the camera detection and LiDAR detection confidence, and the water surface target is determined by combining the detection thresholds. Finally, the bounding box is used to determine the 3D point clouds of the target and estimate its 3D coordinates. The experiment results showed this method reduced the misidentification rate and had 15.5% higher accuracy compared with traditional CornerNet-Lite network. By combining the depth information from LiDAR, the position of the target relative to the detection coordinate system origin could be accurately estimated. Full article

(This article belongs to the Special Issue Multi‐Sensors for Indoor Localization and Tracking)

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16 pages, 2530 KiB

Open AccessArticle

An Adaptive Calibration Algorithm Based on RSSI and LDPLM for Indoor Ranging and Positioning

by Jingmin Yang, Shanghui Deng, Minmin Lin and Li Xu

Sensors 2022, 22(15), 5689; https://doi.org/10.3390/s22155689 - 29 Jul 2022

Cited by 3 | Viewed by 1735

Abstract

The positioning algorithm based on received signal strength indication (RSSI) and the logarithmic distance path loss model (LDPLM) is widely used in indoor positioning scenarios due to its convenient detection and low costs. However, the classic LDPLM with fixed coefficients and fixed error estimation usually reduces the ranging accuracy, but it is rarely studied in previous literature. This study proposes an adaptive calibration ranging algorithm based on LDPLM, which consists of two parts: coefficient adaptive algorithm and error correction algorithm. The coefficient adaptive algorithm is derived by utilizing the error theory and the least squares method. The error correction algorithm is defined as the linear regression equation, in which coefficients are determined by the least squares method. In addition, to reduce the influence of RSSI’s fluctuation on ranging accuracy, we propose a simple but effective filtering algorithm based on Gaussian. The experimental results show that compared with the classic LDPLM and polynomial fitting model, the ranging accuracy of the proposed algorithm is improved by 58% and 51%, respectively, and the positioning cumulative prediction error of the proposed model is reduced by 69% and 80%, respectively. Full article

(This article belongs to the Special Issue Multi‐Sensors for Indoor Localization and Tracking)

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18 pages, 8751 KiB

Open AccessArticle

Lightweight Indoor Multi-Object Tracking in Overlapping FOV Multi-Camera Environments

by Jungik Jang, Minjae Seon and Jaehyuk Choi

Sensors 2022, 22(14), 5267; https://doi.org/10.3390/s22145267 - 14 Jul 2022

Cited by 3 | Viewed by 3601

Abstract

Multi-Target Multi-Camera Tracking (MTMCT), which aims to track multiple targets within a multi-camera network, has recently attracted considerable attention due to its wide range of applications. The main challenge of MTMCT is to match local tracklets (i.e., sub-trajectories) obtained by different cameras and to combine them into global trajectories across the multi-camera network. This paper addresses the cross-camera tracklet matching problem in scenarios with partially overlapping fields of view (FOVs), such as indoor multi-camera environments. We present a new lightweight matching method for the MTMC task that employs similarity analysis for location features. The proposed approach comprises two steps: (i) extracting the motion information of targets based on a ground projection method and (ii) matching the tracklets using similarity analysis based on the Dynamic Time Warping (DTW) algorithm. We use a Kanade–Lucas–Tomasi (KLT) algorithm-based frame-skipping method to reduce the computational overhead in object detection and to produce a smooth estimate of the target’s local tracklets. To improve matching accuracy, we also investigate three different location features to determine the most appropriate feature for similarity analysis. The effectiveness of the proposed method has been evaluated through real experiments, demonstrating its ability to accurately match local tracklets. Full article

(This article belongs to the Special Issue Multi‐Sensors for Indoor Localization and Tracking)

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