scholarly journals Indoor Positioning System Based on Chest-Mounted IMU

Sensors ◽  
2019 ◽  
Vol 19 (2) ◽  
pp. 420 ◽  
Author(s):  
Chuanhua Lu ◽  
Hideaki Uchiyama ◽  
Diego Thomas ◽  
Atsushi Shimada ◽  
Rin-ichiro Taniguchi
Keyword(s):  
Particle Filtering ◽  
Dead Reckoning ◽  
Type Systems ◽  
Indoor Navigation ◽  
Location Based Services ◽  
Upper Body ◽  
Measurement Unit ◽  
Navigation Systems ◽  
3D Navigation ◽  
Zero Velocity

Demand for indoor navigation systems has been rapidly increasing with regard to location-based services. As a cost-effective choice, inertial measurement unit (IMU)-based pedestrian dead reckoning (PDR) systems have been developed for years because they do not require external devices to be installed in the environment. In this paper, we propose a PDR system based on a chest-mounted IMU as a novel installation position for body-suit-type systems. Since the IMU is mounted on a part of the upper body, the framework of the zero-velocity update cannot be applied because there are no periodical moments of zero velocity. Therefore, we propose a novel regression model for estimating step lengths only with accelerations to correctly compute step displacement by using the IMU data acquired at the chest. In addition, we integrated the idea of an efficient map-matching algorithm based on particle filtering into our system to improve positioning and heading accuracy. Since our system was designed for 3D navigation, which can estimate position in a multifloor building, we used a barometer to update pedestrian altitude, and the components of our map are designed to explicitly represent building-floor information. With our complete PDR system, we were awarded second place in 10 teams for the IPIN 2018 Competition Track 2, achieving a mean error of 5.2 m after the 800 m walking event.

scholarly journals S-PDR: SBAUPT-Based Pedestrian Dead Reckoning Algorithm for Free-Moving Handheld Devices

Geomatics ◽  
2021 ◽  
Vol 1 (2) ◽  
pp. 148-176
Author(s):  
Maan Khedr ◽  
Naser El-Sheimy
Keyword(s):  
Smart Cities ◽  
Dead Reckoning ◽  
Human Motion ◽  
Indoor Navigation ◽  
Location Based Services ◽  
Navigation Systems ◽  
Short Term ◽  
Navigation Solution ◽  
Correction Technique ◽  
Pedestrian Dead Reckoning

Mobile location-based services (MLBS) are attracting attention for their potential public and personal use for a variety of applications such as location-based advertisement, smart shopping, smart cities, health applications, emergency response, and even gaming. Many of these applications rely on Inertial Navigation Systems (INS) due to the degraded GNSS services indoors. INS-based MLBS using smartphones is hindered by the quality of the MEMS sensors provided in smartphones which suffer from high noise and errors resulting in high drift in the navigation solution rapidly. Pedestrian dead reckoning (PDR) is an INS-based navigation technique that exploits human motion to reduce navigation solution errors, but the errors cannot be eliminated without aid from other techniques. The purpose of this study is to enhance and extend the short-term reliability of PDR systems for smartphones as a standalone system through an enhanced step detection algorithm, a periodic attitude correction technique, and a novel PCA-based motion direction estimation technique. Testing shows that the developed system (S-PDR) provides a reliable short-term navigation solution with a final positioning error that is up to 6 m after 3 min runtime. These results were compared to a PDR solution using an Xsens IMU which is known to be a high grade MEMS IMU and was found to be worse than S-PDR. The findings show that S-PDR can be used to aid GNSS in challenging environments and can be a viable option for short-term indoor navigation until aiding is provided by alternative means. Furthermore, the extended reliable solution of S-PDR can help reduce the operational complexity of aiding navigation systems such as RF-based indoor navigation and magnetic map matching as it reduces the frequency by which these aiding techniques are required and applied.

scholarly journals Audio Guide for Visually Impaired People Based on Combination of Stereo Vision and Musical Tones

Sensors ◽  
2019 ◽  
Vol 20 (1) ◽  
pp. 151 ◽  
Author(s):  
Walter C. S. S. Simões ◽  
Yuri M. L. R. Silva ◽  
José Luiz de S. Pio ◽  
Nasser Jazdi ◽  
Vicente F. de Lucena
Keyword(s):  
Stereo Vision ◽  
Dead Reckoning ◽  
Ground Level ◽  
Indoor Navigation ◽  
Linear Filter ◽  
Navigation Systems ◽  
Delivery Time ◽  
Visually Impaired People ◽  
Margin Of Error ◽  
Pedestrian Dead Reckoning

Indoor navigation systems offer many application possibilities for people who need information about the scenery and the possible fixed and mobile obstacles placed along the paths. In these systems, the main factors considered for their construction and evaluation are the level of accuracy and the delivery time of the information. However, it is necessary to notice obstacles placed above the user’s waistline to avoid accidents and collisions. In this paper, different methodologies are associated to define a hybrid navigation model called iterative pedestrian dead reckoning (i-PDR). i-PDR combines the PDR algorithm with a Kalman linear filter to correct the location, reducing the system’s margin of error iteratively. Obstacle perception was addressed through the use of stereo vision combined with a musical sounding scheme and spoken instructions that covered an angle of 120 degrees in front of the user. The results obtained in the margin of error and the maximum processing time are 0.70 m and 0.09 s, respectively, with obstacles at ground level and suspended with an accuracy equivalent to 90%.

An Intelligent Navigation Solution for Land Mobile Location-Based Services

2002 ◽  
Vol 55 (2) ◽  
pp. 225-240 ◽  
Author(s):  
Stephen Scott-Young ◽  
Allison Kealy
Keyword(s):  
Firm Growth ◽  
Spatial Information ◽  
Inertial Sensors ◽  
Low Cost ◽  
Dead Reckoning ◽  
Location Based Services ◽  
Geographical Information ◽  
Navigation Systems ◽  
Navigation Solution ◽  
Lower Accuracy

The increasing availability of small, low-cost GPS receivers has established a firm growth in the production of Location-Based Services (LBS). LBS, such as in-car navigation systems, are not necessarily reliant on high accuracy but a continuous positioning service. When available, the accuracy provided by the standard positioning service (SPS) of 30 metres, 95% of the time is often acceptable. The reality is, however, that GPS does not work in all situations, and it is therefore common to integrate GPS with additional sensors. The use of low-cost inertial sensors alone during GPS signal outage is severely restricted due to the accumulation of errors that is inherent with such dead reckoning (DR) systems. Through the integration of spatial information with real-time positioning sensors, intelligence can be added to the land mobile navigation solution. The information contained within a Geographical Information System (GIS) provides additional observations that can be used to improve the navigation result. With this approach, the solution is not dependent on the performance capabilities of the navigation sensors alone. This enables the use of lower accuracy navigation devices, allowing low-cost systems to provide a sustained, viable navigation solution despite long-term GPS outages. Practical results are presented comparing solutions obtained from a hand-held GPS receiver to a gyroscope and odometer.

scholarly journals A FRAMEWORK OF COGNITIVE INDOOR NAVIGATION BASED ON CHARACTERISTICS OF INDOOR SPATIAL ENVIRONMENT

2015 ◽  
Vol XL-4/W5 ◽  
pp. 25-28
Author(s):  
R. Si ◽  
M. Arikawa
Keyword(s):  
Spatial Cognition ◽  
User Interfaces ◽  
Indoor Navigation ◽  
Location Based Services ◽  
Outdoor Environment ◽  
Navigation Systems ◽  
Spatial Environment ◽  
Map Design ◽  
Indoor Navigation System ◽  
Automatic Positioning

People are easy to get confused in indoor spatial environment. Thus, indoor navigation systems on mobile devices are expected in a wide variety of application domains. Limited by the accuracy of indoor positioning, indoor navigating systems are not common in our society. However, automatic positioning is not all about location-based services (LBS), other factors, such as good map design and user interfaces, are also important to satisfy users of LBS. Indoor spatial environment and people’s indoor spatial cognition are different than those in outdoor environment, which asks for different design of LBS. This paper introduces our design methods of indoor navigation system based on the characteristics of indoor spatial environment and indoor spatial cognition.

scholarly journals A Zero-Velocity Update Method for Adaptive Particle Filtering

2020 ◽  
Vol 38 (2) ◽  
pp. 427-433
Author(s):  
Yuyang Guo ◽  
Xiangbo Xu ◽  
Miaoxin Ji
Keyword(s):  
Kalman Filter ◽  
Particle Filter ◽  
Particle Filtering ◽  
State Space Model ◽  
Classical Particle ◽  
Measurement Unit ◽  
Correction Algorithm ◽  
Zero Velocity ◽  
Non Gaussian ◽  
Navigation Errors

Aiming at the low precision of Kalman filter in dealing with non-linear and non-Gaussian models and the serious particle degradation in standard particle filter, a zero-velocity correction algorithm of adaptive particle filter is proposed in this paper. In order to improve the efficiency of resampling, the adaptive threshold is combined with particle filter. In the process of resampling, the degradation co-efficient is introduced to judge the degree of particle degradation, and the particles are re-sampled to ensure the diversity of particles. In order to verify the effectiveness and feasibility of the proposed algorithm, a hardware platform based on the inertial measurement unit (IMU) is built, and the state space model of the system is established by using the data collected by IMU, and experiments are carried out. The experimental results show that, compared with Kalman filter and classical particle filter, the positioning accuracy of adaptive particle filter in zero-velocity range is improved by 40.6% and 19.4% respectively. The adaptive particle filter (APF) can correct navigation errors better and improve pedestrian trajectory accuracy.

scholarly journals A Simplified Method of Cartographic Visualisation of Buildings’ Interiors (2D+) for Navigation Applications

2020 ◽  
Vol 9 (6) ◽  
pp. 407
Author(s):  
Dariusz Gotlib ◽  
Michał Wyszomirski ◽  
Miłosz Gnat
Keyword(s):  
Computer Aided Design ◽  
3D Model ◽  
Three Dimensional ◽  
Original Method ◽  
Facility Management ◽  
Indoor Navigation ◽  
Location Based Services ◽  
Navigation Systems ◽  
Building Information ◽  
The One

This article proposes an original method of a coherent and simplified cartographic presentation of the interior of buildings called 2D+, which can be used in geoinformation applications that do not support an extensive three-dimensional visualisation or do not have access to a 3D model of the building. A simplified way of cartographic visualisation can be used primarily in indoor navigation systems and other location-based services (LBS) applications. It can also be useful in systems supporting facility management (FM) and various kinds of geographic information systems (GIS). On the one hand, it may increase an application’s efficiency; on the other, it may unify the method of visualisation in the absence of a building’s 3D model. Thanks to the proposed method, it is possible to achieve the same effect regardless of the data source used: Building Information Modelling (BIM), a Computer-aided Design (CAD) model, or traditional architectural and construction drawings. Such a solution may be part of a broader concept of a multi-scale presentation of buildings’ interiors. The article discusses the issues of visualising data and converting data to the appropriate coordinate system, as well as the properties of the application model of data.

scholarly journals Toward Accurate Position Estimation Using Learning to Prediction Algorithm in Indoor Navigation

Sensors ◽  
2020 ◽  
Vol 20 (16) ◽  
pp. 4410 ◽  
Author(s):  
Faisal Jamil ◽  
Naeem Iqbal ◽  
Shabir Ahmad ◽  
Do-Hyeun Kim
Keyword(s):  
Kalman Filter ◽  
Indoor Localization ◽  
Position Estimation ◽  
Indoor Navigation ◽  
Location Based Services ◽  
Prediction Algorithm ◽  
Measurement Unit ◽  
Sensor Reading ◽  
Learning Module ◽  
Estimation System

Internet of Things is advancing, and the augmented role of smart navigation in automating processes is at its vanguard. Smart navigation and location tracking systems are finding increasing use in the area of the mission-critical indoor scenario, logistics, medicine, and security. A demanding emerging area is an Indoor Localization due to the increased fascination towards location-based services. Numerous inertial assessments unit-based indoor localization mechanisms have been suggested in this regard. However, these methods have many shortcomings pertaining to accuracy and consistency. In this study, we propose a novel position estimation system based on learning to the prediction model to address the above challenges. The designed system consists of two modules; learning to prediction module and position estimation using sensor fusion in an indoor environment. The prediction algorithm is attached to the learning module. Moreover, the learning module continuously controls, observes, and enhances the efficiency of the prediction algorithm by evaluating the output and taking into account the exogenous factors that may have an impact on its outcome. On top of that, we reckon a situation where the prediction algorithm can be applied to anticipate the accurate gyroscope and accelerometer reading from the noisy sensor readings. In the designed system, we consider a scenario where the learning module, based on Artificial Neural Network, and Kalman filter are used as a prediction algorithm to predict the actual accelerometer and gyroscope reading from the noisy sensor reading. Moreover, to acquire data, we use the next-generation inertial measurement unit, which contains a 3-axis accelerometer and gyroscope data. Finally, for the performance and accuracy of the proposed system, we carried out numbers of experiments, and we observed that the proposed Kalman filter with learning module performed better than the traditional Kalman filter algorithm in terms of root mean square error metric.

scholarly journals Evaluation of AHRS algorithms for Foot-Mounted Inertial-based Indoor Navigation Systems

2019 ◽  
Vol 11 (1) ◽  
pp. 48-63 ◽  
Author(s):  
Xin Li ◽  
Yang Wang
Keyword(s):  
Angular Rate ◽  
Dead Reckoning ◽  
Threshold Value ◽  
Detection Algorithm ◽  
Indoor Navigation ◽  
Assistive Technologies ◽  
Navigation Systems ◽  
Positioning Accuracy ◽  
Time Period ◽  
Open Datasets

Abstract Personal Dead Reckoning based on foot-mounted Inertial Measurement Units is a research hotspot in the field of positioning and navigation in recent years. This paper conducts a targeted research on the application of current mainstream attitude and heading reference system (AHRS) algorithm in the foot inertial navigation positioning. Through open datasets, the positioning accuracy and directional accuracy of 9-state complementary Kalman filter (CKF) are compared and analyzed among the conventional algorithm, Mahony algorithm, and Madgwick algorithm, in which the Madgwick algorithm can achieve the best positioning results. And on this basis, for the Madgwick algorithm, it is verified that it can help improve the positioning accuracy of 15-state CKF under the assistive technologies of zero angular rate update (ZARU) and heuristic heading reduction (HDR). The adaptive zerospeed detection algorithm is designed, and the threshold value of zero-speed detection is set dynamically through tracking the variable of speed in CKF, which can detect the time period of zero-speed state more accurately, thus further improving the correction of directional errors. Finally, the effectiveness of the proposed algorithm is further proved by actual data.

scholarly journals Indoor positioning based on foot-mounted IMU

2015 ◽  
Vol 63 (3) ◽  
pp. 629-634 ◽  
Author(s):  
H. Guo ◽  
M. Uradzinski ◽  
H. Yin ◽  
M. Yu
Keyword(s):  
Sensor Fusion ◽  
Dead Reckoning ◽  
Indoor Positioning ◽  
Indoor Navigation ◽  
Navigation Systems ◽  
Initial Alignment ◽  
Step Detection ◽  
Accumulated Error ◽  
Pedestrian Dead Reckoning ◽  
Average Position

Abstract The paper presents the results of the project which examines the level of accuracy that can be achieved in precision indoor positioning by using a pedestrian dead reckoning (PDR) method. This project is focused on estimating the position using step detection technique based on foot-mounted IMU. The approach is sensor-fusion by using accelerometers, gyroscopes and magnetometers after initial alignment is completed. By estimating and compensating the drift errors in each step, the proposed method can reduce errors during the footsteps. There is an advantage of the step detection combined with ZUPT and ZARU for calculating the actual position, distance travelled and estimating the IMU sensors’ inherent accumulated error by EKF. Based on the above discussion, all algorithms are derived in detail in the paper. Several tests with an Xsens IMU device have been performed in order to evaluate the performance of the proposed method. The final results show that the dead reckoning positioning average position error did not exceed 0.88 m (0.2% to 1.73% of the total traveled distance – normally ranges from 0.3% to 10%), what is very promising for future handheld indoor navigation systems that can be used in large office buildings, malls, museums, hospitals, etc.

scholarly journals RETHINKING INDOOR LOCALIZATION SOLUTIONS TOWARDS THE FUTURE OF MOBILE LOCATION-BASED SERVICES

2017 ◽  
Vol IV-4/W4 ◽  
pp. 235-247 ◽  
Author(s):  
C. Guney
Keyword(s):  
Indoor Localization ◽  
Smart Cities ◽  
Indoor Navigation ◽  
Location Based Services ◽  
Outdoor Environment ◽  
Indoor Environments ◽  
Navigation Systems ◽  
Positioning Systems ◽  
Indoor Location ◽  
High Level

Satellite navigation systems with GNSS-enabled devices, such as smartphones, car navigation systems, have changed the way users travel in outdoor environment. GNSS is generally not well suited for indoor location and navigation because of two reasons: First, GNSS does not provide a high level of accuracy although indoor applications need higher accuracies. Secondly, poor coverage of satellite signals for indoor environments decreases its accuracy. So rather than using GNSS satellites within closed environments, existing indoor navigation solutions rely heavily on installed sensor networks. There is a high demand for accurate positioning in wireless networks in GNSS-denied environments. However, current wireless indoor positioning systems cannot satisfy the challenging needs of indoor location-aware applications. Nevertheless, access to a user’s location indoors is increasingly important in the development of context-aware applications that increases business efficiency. In this study, how can the current wireless location sensing systems be tailored and integrated for specific applications, like smart cities/grids/buildings/cars and IoT applications, in GNSS-deprived areas.

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