Next Generation MEMS-Based Navigation—Systems and Applications

A special issue of Micromachines (ISSN 2072-666X).

Deadline for manuscript submissions: closed (31 May 2015) | Viewed by 106901

Special Issue Editors


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Guest Editor
Department of Geomatics Engineering, The University of Calgary, Calgary, AB T2N 1N4, Canada
Interests: intelligent and autonomous systems; navigation & positioning technologies; satellite technologies; multi-sensor systems; wireless positioning; vehicles & transportation systems; driverless cars; technology development; applications
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Guest Editor
Department of Electrical and Computer Engineering, Royal Military College of Canada, Queen’s University, 19 General Crerar Crescent, S5214, Sawyer Building, Module 2, Kingston, ON K7K 7B4, Canada
Interests: Wireless location and navigation; global navigation satellite systems (GNSS); inertial navigation systems (INS); multi-sensor fusion involving GNSS, INS, Radar, LiDAR, and vision systems for positioning and navigation; optimal estimation; artificial intelligence, positioning in challenging and denied GNSS environments including urban areas, indoors and under jamming conditions
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues

The promise of Micro-Electro-Mechanical Systems (MEMS) technology to the navigation community has been germinating over the last decade, and current advances bring the field to the very cusp of fruition. In fact, MEMS inertial sensors and systems have become indispensable to the future of navigation. Also, navigation itself has become much broader than just providing a solution to the question “Where am I?” or “How to go there?” It has moved into new areas such as games, geolocation, mobile mapping, virtual reality, tracking health monitoring and context awareness; all of which have been enabled by MEMS technology. Their small size, low power and weight, and low cost have led to increased use, new applications, increased mobility, increased integration (hence better performance) and extended operation. When integrated with global navigation satellite systems (GNSS) and other sensors, the integrated system enhance the performance in denied GNSS environments where the satellite signal is either totally blocked or attenuated. The combination of the two systems exploits their complementary characteristics. These integrated navigation technologies and methods have become indispensable in many applications like car navigation, human motion modeling, first-responder personal navigation, UAV, and portable navigation.

This special issue targets original research that addresses the development of next-generation MEMS-based navigation technologies which integrate MEMS sensors and systems to increase the accuracy, reliability and availability of the navigation solution for current and future navigation applications. The special issue invites original research papers addressing, but not limited to:

  • Advances in MEMS inertial sensors and systems;
  • Applications and integration with other sensors (e.g. GNSS, WiFi, barometers, magnetometers, cameras, LiDAR, odometers, etc.);
  • Advanced estimation algorithms for MEMS integrated navigation applications;
  • Design, calibration, modeling, advanced processing techniques and performance characteristics of different technologies for MEMS Sensors;
  • Navigation in indoor, urban or GNSS-degraded environments;
  • Mapping and LBS applications with MEMS sensors.

Prof. Dr. Naser El-Sheimy
Prof. Dr. Aboelmagd Noureldin
Guest Editors

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Keywords

  • MEMS
  • integrated navigation systems
  • INS, GNSS, LiDAR
  • vision cameras
  • radar systems
  • data fusion
  • Kalman Filtering
  • artificial intelligence
  • particle filtering
  • smartphone sensors
  • portable navigation
  • car navigation
  • UGV and UAV
  • POS/NAV

Published Papers (13 papers)

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Research

25362 KiB  
Article
Activity Recognition Using Fusion of Low-Cost Sensors on a Smartphone for Mobile Navigation Application
by Sara Saeedi and Naser El-Sheimy
Micromachines 2015, 6(8), 1100-1134; https://doi.org/10.3390/mi6081100 - 14 Aug 2015
Cited by 38 | Viewed by 11453
Abstract
Low-cost inertial and motion sensors embedded on smartphones have provided a new platform for dynamic activity pattern inference. In this research, a comparison has been conducted on different sensor data, feature spaces and feature selection methods to increase the efficiency and reduce the [...] Read more.
Low-cost inertial and motion sensors embedded on smartphones have provided a new platform for dynamic activity pattern inference. In this research, a comparison has been conducted on different sensor data, feature spaces and feature selection methods to increase the efficiency and reduce the computation cost of activity recognition on the smartphones. We evaluated a variety of feature spaces and a number of classification algorithms from the area of Machine Learning, including Naive Bayes, Decision Trees, Artificial Neural Networks and Support Vector Machine classifiers. A smartphone app that performs activity recognition is being developed to collect data and send them to a server for activity recognition. Using extensive experiments, the performance of various feature spaces has been evaluated. The results showed that the Bayesian Network classifier yields recognition accuracy of 96.21% using four features while requiring fewer computations. Full article
(This article belongs to the Special Issue Next Generation MEMS-Based Navigation—Systems and Applications)
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6897 KiB  
Article
Biomimetic-Based Output Feedback for Attitude Stabilization of Rigid Bodies: Real-Time Experimentation on a Quadrotor
by José Fermi Guerrero-Castellanos, Hala Rifaï, Nicolas Marchand, Rafael Cruz-José, Samer Mohammed, W. Fermín Guerrero-Sánchez and Gerardo Mino-Aguilar
Micromachines 2015, 6(8), 993-1022; https://doi.org/10.3390/mi6080993 - 05 Aug 2015
Viewed by 5452
Abstract
The present paper deals with the development of bounded feedback control laws mimicking the strategy adopted by flapping flyers to stabilize the attitude of systems falling within the framework of rigid bodies. Flapping flyers are able to orient their trajectory without any knowledge [...] Read more.
The present paper deals with the development of bounded feedback control laws mimicking the strategy adopted by flapping flyers to stabilize the attitude of systems falling within the framework of rigid bodies. Flapping flyers are able to orient their trajectory without any knowledge of their current attitude and without any attitude computation. They rely on the measurements of some sensitive organs: halteres, leg sensilla and magnetic sense, which give information about their angular velocity and the orientation of gravity and magnetic field vectors. Therefore, the proposed feedback laws are computed using direct inertial sensors measurements, that is vector observations with/without angular velocity measurements. Hence, the attitude is not explicitly required. This biomimetic approach is very simple, requires little computational power and is suitable for embedded applications on small control units. The boundedness of the control signal is taken into consideration through the design of the control laws by saturation of the actuators’ input. The asymptotic stability of the closed loop system is proven by Lyapunov analysis. Real-time experiments are carried out on a quadrotor using MEMS inertial sensors in order to emphasize the efficiency of this biomimetic strategy by showing the convergence of the body’s states in hovering mode, as well as the robustness with respect to external disturbances. Full article
(This article belongs to the Special Issue Next Generation MEMS-Based Navigation—Systems and Applications)
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2153 KiB  
Article
A Novel Kalman Filter with State Constraint Approach for the Integration of Multiple Pedestrian Navigation Systems
by Haiyu Lan, Chunyang Yu, Yuan Zhuang, You Li and Naser El-Sheimy
Micromachines 2015, 6(7), 926-952; https://doi.org/10.3390/mi6070926 - 16 Jul 2015
Cited by 33 | Viewed by 6803
Abstract
Numerous solutions/methods to solve the existing problems of pedestrian navigation/localization have been proposed in the last decade by both industrial and academic researchers. However, to date there are still major challenges for a single pedestrian navigation system (PNS) to operate continuously, robustly, and [...] Read more.
Numerous solutions/methods to solve the existing problems of pedestrian navigation/localization have been proposed in the last decade by both industrial and academic researchers. However, to date there are still major challenges for a single pedestrian navigation system (PNS) to operate continuously, robustly, and seamlessly in all indoor and outdoor environments. In this paper, a novel method for pedestrian navigation approach to fuse the information from two separate PNSs is proposed. When both systems are used at the same time by a specific user, a nonlinear inequality constraint between the two systems’ navigation estimates always exists. Through exploring this constraint information, a novel filtering technique named Kalman filter with state constraint is used to diminish the positioning errors of both systems. The proposed method was tested by fusing the navigation information from two different PNSs, one is the foot-mounted inertial navigation system (INS) mechanization-based system, the other PNS is a navigation device that is mounted on the user’s upper body, and adopting the pedestrian dead reckoning (PDR) mechanization for navigation update. Monte Carlo simulations and real field experiments show that the proposed method for the integration of multiple PNSs could improve each PNS’ navigation performance. Full article
(This article belongs to the Special Issue Next Generation MEMS-Based Navigation—Systems and Applications)
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2806 KiB  
Article
PDR/INS/WiFi Integration Based on Handheld Devices for Indoor Pedestrian Navigation
by Yuan Zhuang, Haiyu Lan, You Li and Naser El-Sheimy
Micromachines 2015, 6(6), 793-812; https://doi.org/10.3390/mi6060793 - 23 Jun 2015
Cited by 109 | Viewed by 9187
Abstract
Providing an accurate and practical navigation solution anywhere with portable devices, such as smartphones, is still a challenge, especially in environments where global navigation satellite systems (GNSS) signals are not available or are degraded. This paper proposes a new algorithm that integrates inertial [...] Read more.
Providing an accurate and practical navigation solution anywhere with portable devices, such as smartphones, is still a challenge, especially in environments where global navigation satellite systems (GNSS) signals are not available or are degraded. This paper proposes a new algorithm that integrates inertial navigation system (INS) and pedestrian dead reckoning (PDR) to combine the advantages of both mechanizations for micro-electro-mechanical systems (MEMS) sensors in pedestrian navigation applications. In this PDR/INS integration algorithm, a pseudo-velocity-vector, which is composed of the PDR-derived forward speed and zero lateral and vertical speeds from non-holonomic constraints (NHC), works as an update for the INS to limit the velocity errors. To further limit the drift of MEMS inertial sensors, trilateration-based WiFi positions with small variances are also selected as updates for the PDR/INS integrated system. The experiments illustrate that positioning error is decreased by 60%–75% by using the proposed PDR/INS integrated MEMS solution when compared with PDR. The positioning error is further decreased by 15%–55% if the proposed PDR/INS/WiFi integrated solution is implemented. The average accuracy of the proposed PDR/INS/WiFi integration algorithm achieves 4.5 m in indoor environments. Full article
(This article belongs to the Special Issue Next Generation MEMS-Based Navigation—Systems and Applications)
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4107 KiB  
Article
WiFi-Aided Magnetic Matching for Indoor Navigation with Consumer Portable Devices
by You Li, Yuan Zhuang, Haiyu Lan, Peng Zhang, Xiaoji Niu and Naser El-Sheimy
Micromachines 2015, 6(6), 747-764; https://doi.org/10.3390/mi6060747 - 16 Jun 2015
Cited by 60 | Viewed by 7980
Abstract
This paper presents a WiFi-aided magnetic matching (MM) algorithm for indoor pedestrian navigation with consumer portable devices. This algorithm reduces both the mismatching rate (i.e., the rate of matching to an incorrect point that is more than 20 m away from [...] Read more.
This paper presents a WiFi-aided magnetic matching (MM) algorithm for indoor pedestrian navigation with consumer portable devices. This algorithm reduces both the mismatching rate (i.e., the rate of matching to an incorrect point that is more than 20 m away from the true value) and computational load of MM by using WiFi positioning solutions to limit the MM search space. Walking tests with Samsung Galaxy S3 and S4 smartphones in two different indoor environments (i.e., Environment #1 with abundant WiFi APs and significant magnetic features, and Environment #2 with less WiFi and magnetic information) were conducted to evaluate the proposed algorithm. It was found that WiFi fingerprinting accuracy is related to the signal distributions. MM provided results with small fluctuations but had a significant mismatch rate; when aided by WiFi, MM’s robustness was significantly improved. The outcome of this research indicates that WiFi and MM have complementary characteristics as the former is a point-by-point matching approach and the latter is based on profile-matching. Furthermore, performance improvement through integrating WiFi and MM depends on the environment (e.g., the signal distributions of magnetic intensity and WiFi RSS): In Environment #1 tests, WiFi-aided MM and WiFi provided similar results; in Environment #2 tests, the former was approximately 41.6% better. Our results supported that the WiFi-aided MM algorithm provided more reliable solutions than both WiFi and MM in the areas that have poor WiFi signal distribution or indistinctive magnetic-gradient features. Full article
(This article belongs to the Special Issue Next Generation MEMS-Based Navigation—Systems and Applications)
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4745 KiB  
Article
Reciprocal Estimation of Pedestrian Location and Motion State toward a Smartphone Geo-Context Computing Solution
by Jingbin Liu, Lingli Zhu, Yunsheng Wang, Xinlian Liang, Juha Hyyppä, Tianxing Chu, Keqiang Liu and Ruizhi Chen
Micromachines 2015, 6(6), 699-717; https://doi.org/10.3390/mi6060699 - 15 Jun 2015
Cited by 10 | Viewed by 6565
Abstract
The rapid advance in mobile communications has made information and services ubiquitously accessible. Location and context information have become essential for the effectiveness of services in the era of mobility. This paper proposes the concept of geo-context that is defined as an integral [...] Read more.
The rapid advance in mobile communications has made information and services ubiquitously accessible. Location and context information have become essential for the effectiveness of services in the era of mobility. This paper proposes the concept of geo-context that is defined as an integral synthesis of geographical location, human motion state and mobility context. A geo-context computing solution consists of a positioning engine, a motion state recognition engine, and a context inference component. In the geo-context concept, the human motion states and mobility context are associated with the geographical location where they occur. A hybrid geo-context computing solution is implemented that runs on a smartphone, and it utilizes measurements of multiple sensors and signals of opportunity that are available within a smartphone. Pedestrian location and motion states are estimated jointly under the framework of hidden Markov models, and they are used in a reciprocal manner to improve their estimation performance of one another. It is demonstrated that pedestrian location estimation has better accuracy when its motion state is known, and in turn, the performance of motion state recognition can be improved with increasing reliability when the location is given. The geo-context inference is implemented simply with the expert system principle, and more sophisticated approaches will be developed. Full article
(This article belongs to the Special Issue Next Generation MEMS-Based Navigation—Systems and Applications)
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1161 KiB  
Article
Signal Processing Technique for Combining Numerous MEMS Gyroscopes Based on Dynamic Conditional Correlation
by Jieyu Liu, Qiang Shen and Weiwei Qin
Micromachines 2015, 6(6), 684-698; https://doi.org/10.3390/mi6060684 - 12 Jun 2015
Cited by 15 | Viewed by 6171
Abstract
A signal processing technique is presented to improve the angular rate accuracy of Micro-Electro-Mechanical System (MEMS) gyroscope by combining numerous gyroscopes. Based on the conditional correlation between gyroscopes, a dynamic data fusion model is established. Firstly, the gyroscope error model is built through [...] Read more.
A signal processing technique is presented to improve the angular rate accuracy of Micro-Electro-Mechanical System (MEMS) gyroscope by combining numerous gyroscopes. Based on the conditional correlation between gyroscopes, a dynamic data fusion model is established. Firstly, the gyroscope error model is built through Generalized Autoregressive Conditional Heteroskedasticity (GARCH) process to improve overall performance. Then the conditional covariance obtained through dynamic conditional correlation (DCC) estimator is used to describe the correlation quantitatively. Finally, the approach is validated by a prototype of the virtual gyroscope, which consists of six-gyroscope array. The experimental results indicate that the weights of gyroscopes change with the value of error. Also, the accuracy of combined rate signal is improved dramatically compared to individual gyroscope. The results indicate that the approach not only improves the accuracy of the MEMS gyroscope, but also discovers the fault gyroscope and eliminates its influence. Full article
(This article belongs to the Special Issue Next Generation MEMS-Based Navigation—Systems and Applications)
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1707 KiB  
Article
Extended Kalman Filter for Real Time Indoor Localization by Fusing WiFi and Smartphone Inertial Sensors
by Zhi-An Deng, Ying Hu, Jianguo Yu and Zhenyu Na
Micromachines 2015, 6(4), 523-543; https://doi.org/10.3390/mi6040523 - 22 Apr 2015
Cited by 104 | Viewed by 10689
Abstract
Indoor localization systems using WiFi received signal strength (RSS) or pedestrian dead reckoning (PDR) both have their limitations, such as the RSS fluctuation and the accumulative error of PDR. To exploit their complementary strengths, most existing approaches fuse both systems by a particle [...] Read more.
Indoor localization systems using WiFi received signal strength (RSS) or pedestrian dead reckoning (PDR) both have their limitations, such as the RSS fluctuation and the accumulative error of PDR. To exploit their complementary strengths, most existing approaches fuse both systems by a particle filter. However, the particle filter is unsuitable for real time localization on resource-limited smartphones, since it is rather time-consuming and computationally expensive. On the other hand, the light computation fusion approaches including Kalman filter and its variants are inapplicable, since an explicit RSS-location measurement equation and the related noise statistics are unavailable. This paper proposes a novel data fusion framework by using an extended Kalman filter (EKF) to integrate WiFi localization with PDR. To make EKF applicable, we develop a measurement model based on kernel density estimation, which enables accurate WiFi localization and adaptive measurement noise statistics estimation. For the PDR system, we design another EKF based on quaternions for heading estimation by fusing gyroscopes and accelerometers. Experimental results show that the proposed EKF based data fusion approach achieves significant localization accuracy improvement over using WiFi localization or PDR systems alone. Compared with a particle filter, the proposed approach achieves comparable localization accuracy, while it incurs much less computational complexity. Full article
(This article belongs to the Special Issue Next Generation MEMS-Based Navigation—Systems and Applications)
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2589 KiB  
Article
Invariant Observer-Based State Estimation for Micro-Aerial Vehicles in GPS-Denied Indoor Environments Using an RGB-D Camera and MEMS Inertial Sensors
by Dachuan Li, Qing Li, Liangwen Tang, Sheng Yang, Nong Cheng and Jingyan Song
Micromachines 2015, 6(4), 487-522; https://doi.org/10.3390/mi6040487 - 22 Apr 2015
Cited by 13 | Viewed by 9787
Abstract
This paper presents a non-linear state observer-based integrated navigation scheme for estimating the attitude, position and velocity of micro aerial vehicles (MAV) operating in GPS-denied indoor environments, using the measurements from low-cost MEMS (micro electro-mechanical systems) inertial sensors and an RGB-D camera. A [...] Read more.
This paper presents a non-linear state observer-based integrated navigation scheme for estimating the attitude, position and velocity of micro aerial vehicles (MAV) operating in GPS-denied indoor environments, using the measurements from low-cost MEMS (micro electro-mechanical systems) inertial sensors and an RGB-D camera. A robust RGB-D visual odometry (VO) approach was developed to estimate the MAV’s relative motion by extracting and matching features captured by the RGB-D camera from the environment. The state observer of the RGB-D visual-aided inertial navigation was then designed based on the invariant observer theory for systems possessing symmetries. The motion estimates from the RGB-D VO were fused with inertial and magnetic measurements from the onboard MEMS sensors via the state observer, providing the MAV with accurate estimates of its full six degree-of-freedom states. Implementations on a quadrotor MAV and indoor flight test results demonstrate that the resulting state observer is effective in estimating the MAV’s states without relying on external navigation aids such as GPS. The properties of computational efficiency and simplicity in gain tuning make the proposed invariant observer-based navigation scheme appealing for actual MAV applications in indoor environments. Full article
(This article belongs to the Special Issue Next Generation MEMS-Based Navigation—Systems and Applications)
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1729 KiB  
Article
Smartphone-Based Indoor Integrated WiFi/MEMS Positioning Algorithm in a Multi-Floor Environment
by Zengshan Tian, Xin Fang, Mu Zhou and Lingxia Li
Micromachines 2015, 6(3), 347-363; https://doi.org/10.3390/mi6030347 - 03 Mar 2015
Cited by 39 | Viewed by 8078
Abstract
Indoor positioning in a multi-floor environment by using a smartphone is considered in this paper. The positioning accuracy and robustness of WiFi fingerprinting-based positioning are limited due to the unexpected variation of WiFi measurements between floors. On this basis, we propose a novel [...] Read more.
Indoor positioning in a multi-floor environment by using a smartphone is considered in this paper. The positioning accuracy and robustness of WiFi fingerprinting-based positioning are limited due to the unexpected variation of WiFi measurements between floors. On this basis, we propose a novel smartphone-based integrated WiFi/MEMS positioning algorithm based on the robust extended Kalman filter (EKF). The proposed algorithm first relies on the gait detection approach and quaternion algorithm to estimate the velocity and heading angles of the target. Second, the velocity and heading angles, together with the results of WiFi fingerprinting-based positioning, are considered as the input of the robust EKF for the sake of conducting two-dimensional (2D) positioning. Third, the proposed algorithm calculates the height of the target by using the real-time recorded barometer and geographic data. Finally, the experimental results show that the proposed algorithm achieves the positioning accuracy with root mean square errors (RMSEs) less than 1 m in an actual multi-floor environment. Full article
(This article belongs to the Special Issue Next Generation MEMS-Based Navigation—Systems and Applications)
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483 KiB  
Article
Noise Reduction of MEMS Gyroscope Based on Direct Modeling for an Angular Rate Signal
by Liang Xue, Chengyu Jiang, Lixin Wang, Jieyu Liu and Weizheng Yuan
Micromachines 2015, 6(2), 266-280; https://doi.org/10.3390/mi6020266 - 16 Feb 2015
Cited by 21 | Viewed by 9013
Abstract
In this paper, a novel approach for processing the outputs signal of the microelectromechanical systems (MEMS) gyroscopes was presented to reduce the bias drift and noise. The principle for the noise reduction was presented, and an optimal Kalman filter (KF) was designed by [...] Read more.
In this paper, a novel approach for processing the outputs signal of the microelectromechanical systems (MEMS) gyroscopes was presented to reduce the bias drift and noise. The principle for the noise reduction was presented, and an optimal Kalman filter (KF) was designed by a steady-state filter gain obtained from the analysis of KF observability. In particular, the true angular rate signal was directly modeled to obtain an optimal estimate and make a self-compensation for the gyroscope without needing other sensor’s information, whether in static or dynamic condition. A linear fit equation that describes the relationship between the KF bandwidth and modeling parameter of true angular rate was derived from the analysis of KF frequency response. The test results indicated that the MEMS gyroscope having an ARW noise of 4.87°/h0.5 and a bias instability of 44.41°/h were reduced to 0.4°/h0.5 and 4.13°/h by the KF under a given bandwidth (10 Hz), respectively. The 1σ estimated error was reduced from 1.9°/s to 0.14°/s and 1.7°/s to 0.5°/s in the constant rate test and swing rate test, respectively. It also showed that the filtered angular rate signal could well reflect the dynamic characteristic of the input rate signal in dynamic conditions. The presented algorithm is proved to be effective at improving the measurement precision of the MEMS gyroscope. Full article
(This article belongs to the Special Issue Next Generation MEMS-Based Navigation—Systems and Applications)
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1145 KiB  
Article
Adaptive Covariance Estimation Method for LiDAR-Aided Multi-Sensor Integrated Navigation Systems
by Shifei Liu, Mohamed Maher Atia, Yanbin Gao and Aboelmagd Noureldin
Micromachines 2015, 6(2), 196-215; https://doi.org/10.3390/mi6020196 - 28 Jan 2015
Cited by 19 | Viewed by 7484
Abstract
The accurate estimation of measurements covariance is a fundamental problem in sensors fusion algorithms and is crucial for the proper operation of filtering algorithms. This paper provides an innovative solution for this problem and realizes the proposed solution on a 2D indoor navigation [...] Read more.
The accurate estimation of measurements covariance is a fundamental problem in sensors fusion algorithms and is crucial for the proper operation of filtering algorithms. This paper provides an innovative solution for this problem and realizes the proposed solution on a 2D indoor navigation system for unmanned ground vehicles (UGVs) that fuses measurements from a MEMS-grade gyroscope, speed measurements and a light detection and ranging (LiDAR) sensor. A computationally efficient weighted line extraction method is introduced, where the LiDAR intensity measurements are used, such that the random range errors and systematic errors due to surface reflectivity in LiDAR measurements are considered. The vehicle pose change is obtained from LiDAR line feature matching, and the corresponding pose change covariance is also estimated by a weighted least squares-based technique. The estimated LiDAR-based pose changes are applied as periodic updates to the Inertial Navigation System (INS) in an innovative extended Kalman filter (EKF) design. Besides, the influences of the environment geometry layout and line estimation error are discussed. Real experiments in indoor environment are performed to evaluate the proposed algorithm. The results showed the great consistency between the LiDAR-estimated pose change covariance and the true accuracy. Therefore, this leads to a significant improvement in the vehicle’s integrated navigation accuracy. Full article
(This article belongs to the Special Issue Next Generation MEMS-Based Navigation—Systems and Applications)
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1221 KiB  
Article
A Particle Filter for Smartphone-Based Indoor Pedestrian Navigation
by Andrea Masiero, Alberto Guarnieri, Francesco Pirotti and Antonio Vettore
Micromachines 2014, 5(4), 1012-1033; https://doi.org/10.3390/mi5041012 - 05 Nov 2014
Cited by 51 | Viewed by 6299
Abstract
This paper considers the problem of indoor navigation by means of low-cost mobile devices. The required accuracy, the low reliability of low-cost sensor measurements and the typical unavailability of the GPS signal make indoor navigation a challenging problem. In this paper, a particle [...] Read more.
This paper considers the problem of indoor navigation by means of low-cost mobile devices. The required accuracy, the low reliability of low-cost sensor measurements and the typical unavailability of the GPS signal make indoor navigation a challenging problem. In this paper, a particle filtering approach is presented in order to obtain good navigation performance in an indoor environment: the proposed method is based on the integration of information provided by the inertial navigation system measurements, the radio signal strength of a standard wireless network and of the geometrical information of the building. In order to make the system as simple as possible from the user’s point of view, sensors are assumed to be uncalibrated at the beginning of the navigation, and an auto-calibration procedure of the magnetic sensor is performed to improve the system performance: the proposed calibration procedure is performed during regular user’s motion (no specific work is required). The navigation accuracy achievable with the proposed method and the results of the auto-calibration procedure are evaluated by means of a set of tests carried out in a university building. Full article
(This article belongs to the Special Issue Next Generation MEMS-Based Navigation—Systems and Applications)
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