scholarly journals Applying a ToF/IMU-Based Multi-Sensor Fusion Architecture in Pedestrian Indoor Navigation Methods

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3615
Author(s):  
Farzan Farhangian ◽  
Mohammad Sefidgar ◽  
Rene Landry
Keyword(s):  
Sensor Fusion ◽  
Low Cost ◽  
System Modeling ◽  
Micro Electro Mechanical System ◽  
Indoor Navigation ◽  
Navigation Systems ◽  
Calibration Methods ◽  
Measurement Units ◽  
Distance Sensor ◽  
Imperfect System

The advancement of indoor Inertial Navigation Systems (INS) based on the low-cost Inertial Measurement Units (IMU) has been long reviewed in the field of pedestrian localization. There are various sources of error in these systems which lead to unstable and unreliable positioning results, especially in long term performances. These inaccuracies are usually caused by imperfect system modeling, inappropriate sensor fusion models, heading drift, biases of IMUs, and calibration methods. This article addresses the issues surrounding unreliability of the low-cost Micro-Electro-Mechanical System (MEMS)-based pedestrian INS. We designed a novel multi-sensor fusion method based on a Time of Flight (ToF) distance sensor and dual chest- and foot-mounted IMUs, aided by an online calibration technique. An Extended Kalman Filter (EKF) is accounted for estimating the attitude, position, and velocity errors, as well as estimation of IMU biases. A fusion architecture is derived to provide a consistent velocity measurement by operative contribution of ToF distance sensor and foot mounted IMU. In this method, the measurements of the ToF distance sensor are used for the time-steps in which the Zero Velocity Update (ZUPT) measurements are not active. In parallel, the chest mounted IMU is accounted for attitude estimation of the pedestrian’s chest. As well, by designing a novel corridor detection filter, the heading drift is restricted in each straightway. Compared to the common INS method, developed system proves promising and resilient results in two-dimensional corridor spaces for durations of up to 11 min. Finally, the results of our experiments showed the position RMS error of less than 3 m and final-point error of less than 5 m.

scholarly journals Testing of the Possibilities of Using IMUs with Different Types of Movements

2014 ◽  
Vol 12 ◽  
pp. 61-66 ◽  
Author(s):  
Pavol Kajánek
Keyword(s):  
Inertial Navigation System ◽  
Low Cost ◽  
Indoor Navigation ◽  
Integrated System ◽  
Test Results ◽  
Inertial Measurement Units ◽  
Inertial Measurement ◽  
Improve Accuracy ◽  
Different Types ◽  
Measurement Units

Inertial navigation system (INS) is a self-contained navigation technique. Its main purpose is to determinate the position and the trajectory of the object´s movement in space. This technique is well represented not only as a supplementary method (GPS/INS integrated system) but as an autonomous system for navigation of vehicles and pedestrians, also. The aim of this paper is to design a test for low-cost inertial measurement units. The test results give us information about accuracy, which determine the possible use in indoor navigation or other applications. There are described some methods for processing the data obtained by inertial measurement units, which remove noise and improve accuracy of position and orientation.

scholarly journals INDOOR NAVIGATION ASSISTANCE SYSTEM USING BLUETOOTH

2016 ◽  
pp. 44-45
Author(s):  
APURVA MEHTA ◽  
D. D. PUKALE ◽  
RADHIKA BHAGAT ◽  
RUJAL SHAH
Keyword(s):  
Short Range ◽  
Low Cost ◽  
Indoor Navigation ◽  
Assistance System ◽  
Navigation Systems ◽  
Positioning Systems ◽  
Navigation Assistance ◽  
The Past ◽  
Current Location ◽  
Global Positioning

In the past few years, a number of ideas have been proposed for indoor navigation systems. These ideas were not as widely implemented as outdoor positioning systems like GPS(Global Positioning Systems). We propose an indoor navigation assistance system using Bluetooth which is low cost and feasible to use in daily life. Our system enables users with handheld mobile devices to steer with ease through the indoor premises using the short range radio frequencies of Bluetooth. It also establishes user’s current location and the various paths leading to the destination. Dijkstra’s algorithm is used to determine the shortest path from the source to the required destination.

Using Constraints for Shoe Mounted Indoor Pedestrian Navigation

2011 ◽  
Vol 65 (1) ◽  
pp. 15-28 ◽  
Author(s):  
Khairi Abdulrahim ◽  
Chris Hide ◽  
Terry Moore ◽  
Chris Hill
Keyword(s):  
Kalman Filter ◽  
Average Distance ◽  
Low Cost ◽  
Navigation Systems ◽  
Pedestrian Navigation ◽  
Zero Velocity ◽  
Average Return ◽  
Measurement Units ◽  
Heading Estimation ◽  
Mems Imu

Shoe mounted Inertial Measurement Units (IMU) are often used for indoor pedestrian navigation systems. The presence of a zero velocity condition during the stance phase enables Zero Velocity Updates (ZUPT) to be applied regularly every time the user takes a step. Most of the velocity and attitude errors can be estimated using ZUPTs. However, good heading estimation for such a system remains a challenge. This is due to the poor observability of heading error for a low cost Micro-Electro-Mechanical (MEMS) IMU, even with the use of ZUPTs in a Kalman filter. In this paper, the same approach is adopted where a MEMS IMU is mounted on a shoe, but with additional constraints applied. The three constraints proposed herein are used to generate measurement updates for a Kalman filter, known as ‘Heading Update’, ‘Zero Integrated Heading Rate Update’ and ‘Height Update’.The first constraint involves restricting heading drift in a typical building where the user is walking. Due to the fact that typical buildings are rectangular in shape, an assumption is made that most walking in this environment is constrained to only follow one of the four main headings of the building. A second constraint is further used to restrict heading drift during a non-walking situation. This is carried out because the first constraint cannot be applied when the user is stationary. Finally, the third constraint is applied to limit the error growth in height. An assumption is made that the height changes in indoor buildings are only caused when the user walks up and down a staircase. Several trials were shown to demonstrate the effectiveness of integrating these constraints for indoor pedestrian navigation. The results show that an average return position error of 4·62 meters is obtained for an average distance of 1557 meters using only a low cost MEMS IMU.

A Standard Testing and Calibration Procedure for Low Cost MEMS Inertial Sensors and Units

2008 ◽  
Vol 61 (2) ◽  
pp. 323-336 ◽  
Author(s):  
P. Aggarwal ◽  
Z. Syed ◽  
X. Niu ◽  
N. El-Sheimy
Keyword(s):  
Moving Objects ◽  
Inertial Sensors ◽  
Low Cost ◽  
Calibration Method ◽  
Calibration Procedure ◽  
Measurement Unit ◽  
Navigation Systems ◽  
Integrated Navigation ◽  
Mems Sensors ◽  
Calibration Methods

Navigation involves the integration of methodologies and systems for estimating the time varying position and attitude of moving objects. Inertial Navigation Systems (INS) and the Global Positioning System (GPS) are among the most widely used navigation systems. The use of cost effective MEMS based inertial sensors has made GPS/INS integrated navigation systems more affordable. However MEMS sensors suffer from various errors that have to be calibrated and compensated to get acceptable navigation results. Moreover the performance characteristics of these sensors are highly dependent on the environmental conditions such as temperature variations. Hence there is a need for the development of accurate, reliable and efficient thermal models to reduce the effect of these errors that can potentially degrade the system performance. In this paper, the Allan variance method is used to characterize the noise in the MEMS sensors. A six-position calibration method is applied to estimate the deterministic sensor errors such as bias, scale factor, and non-orthogonality. An efficient thermal variation model is proposed and the effectiveness of the proposed calibration methods is investigated through a kinematic van test using integrated GPS and MEMS-based inertial measurement unit (IMU).

scholarly journals Particle-Filter-Based WiFi-Aided Reduced Inertial Sensors Navigation System for Indoor and GPS-Denied Environments

2012 ◽  
Vol 2012 ◽  
pp. 1-12 ◽  
Author(s):  
M. M. Atia ◽  
M. J. Korenberg ◽  
A. Noureldin
Keyword(s):  
Particle Filter ◽  
Inertial Sensors ◽  
Low Cost ◽  
Indoor Navigation ◽  
Feature Reduction ◽  
Navigation Systems ◽  
Inertial Navigation Systems ◽  
Error Accumulation ◽  
Non Gaussian ◽  
Multipath Errors

Indoor navigation is challenging due to unavailability of satellites-based signals indoors. Inertial Navigation Systems (INSs) may be used as standalone navigation indoors. However, INS suffers from growing drifts without bounds due to error accumulation. On the other side, the IEEE 802.11 WLAN (WiFi) is widely adopted which prompted many researchers to use it to provide positioning indoors using fingerprinting. However, due to WiFi signal noise and multipath errors indoors, WiFi positioning is scattered and noisy. To benefit from both WiFi and inertial systems, in this paper, two major techniques are applied. First, a low-cost Reduced Inertial Sensors System (RISS) is integrated with WiFi to smooth the noisy scattered WiFi positioning and reduce RISS drifts. Second, a fast feature reduction technique is applied to fingerprinting to identify the WiFi access points with highest discrepancy power to be used for positioning. The RISS/WiFi system is implemented using a fast version of Mixture Particle Filter for state estimation as nonlinear non-Gaussian filtering algorithm. Real experiments showed that drifts of RISS are greatly reduced and the scattered noisy WiFi positioning is significantly smoothed. The proposed system provides smooth indoor positioning of 1 m accuracy 70% of the time outperforming each system individually.

scholarly journals DEVELOPMENT OF A PEDESTRIAN INDOOR NAVIGATION SYSTEM BASED ON MULTI-SENSOR FUSION AND FUZZY LOGIC ESTIMATION ALGORITHMS

2015 ◽  
Vol XL-4/W5 ◽  
pp. 81-86 ◽  
Author(s):  
Y. C. Lai ◽  
C. C. Chang ◽  
C. M. Tsai ◽  
S. Y. Lin ◽  
S. C. Huang
Keyword(s):  
Fuzzy Logic ◽  
Sensor Fusion ◽  
Navigation System ◽  
Indoor Environment ◽  
Inertial Sensors ◽  
Low Cost ◽  
Dead Reckoning ◽  
Indoor Navigation ◽  
Estimation Algorithms ◽  
Indoor Navigation System

This paper presents a pedestrian indoor navigation system based on the multi-sensor fusion and fuzzy logic estimation algorithms. The proposed navigation system is a self-contained dead reckoning navigation that means no other outside signal is demanded. In order to achieve the self-contained capability, a portable and wearable inertial measure unit (IMU) has been developed. Its adopted sensors are the low-cost inertial sensors, accelerometer and gyroscope, based on the micro electro-mechanical system (MEMS). There are two types of the IMU modules, handheld and waist-mounted. The low-cost MEMS sensors suffer from various errors due to the results of manufacturing imperfections and other effects. Therefore, a sensor calibration procedure based on the scalar calibration and the least squares methods has been induced in this study to improve the accuracy of the inertial sensors. With the calibrated data acquired from the inertial sensors, the step length and strength of the pedestrian are estimated by multi-sensor fusion and fuzzy logic estimation algorithms. The developed multi-sensor fusion algorithm provides the amount of the walking steps and the strength of each steps in real-time. Consequently, the estimated walking amount and strength per step are taken into the proposed fuzzy logic estimation algorithm to estimates the step lengths of the user. Since the walking length and direction are both the required information of the dead reckoning navigation, the walking direction is calculated by integrating the angular rate acquired by the gyroscope of the developed IMU module. Both the walking length and direction are calculated on the IMU module and transmit to a smartphone with Bluetooth to perform the dead reckoning navigation which is run on a self-developed APP. Due to the error accumulating of dead reckoning navigation, a particle filter and a pre-loaded map of indoor environment have been applied to the APP of the proposed navigation system to extend its usability. The experiment results of the proposed navigation system demonstrate good navigation performance in indoor environment with the accurate initial location and direction.

scholarly journals A NEW SURVEY ON SELF-TUNING INTEGRATED LOW-COST GPS/INS VEHICLE NAVIGATION SYSTEM IN HARSH ENVIRONMENT

2015 ◽  
Vol XL-1/W4 ◽  
pp. 75-81 ◽  
Author(s):  
N. Navidi ◽  
R. Landry
Keyword(s):  
Navigation System ◽  
Fuzzy Inference ◽  
Low Cost ◽  
Navigation Systems ◽  
Micro Electro Mechanical Systems ◽  
Proposed Model ◽  
Self Tuning ◽  
Measurement Units ◽  
Inference Systems ◽  
System Errors

Nowadays, Global Positioning System (GPS) receivers are aided by some complementary radio navigation systems and Inertial Navigation Systems (INS) to obtain more accuracy and robustness in land vehicular navigation. Extended Kalman Filter (EKF) is an acceptable conventional method to estimate the position, the velocity, and the attitude of the navigation system when INS measurements are fused with GPS data. However, the usage of the low-cost Inertial Measurement Units (IMUs) based on the Micro-Electro-Mechanical Systems (MEMS), for the land navigation systems, reduces the precision and stability of the navigation system due to their inherent errors. The main goal of this paper is to provide a new model for fusing low-cost IMU and GPS measurements. The proposed model is based on EKF aided by Fuzzy Inference Systems (FIS) as a promising method to solve the mentioned problems. This model considers the parameters of the measurement noise to adjust the measurement and noise process covariance. The simulation results show the efficiency of the proposed method to reduce the navigation system errors compared with EKF.

scholarly journals A Low Cost Civil Vehicular Seamless Navigation Technology Based on Enhanced RISS/GPS between the Outdoors and an Underground Garage

Electronics ◽  
2020 ◽  
Vol 9 (1) ◽  
pp. 120
Author(s):  
Ningbo Li ◽  
Lianwu Guan ◽  
Yanbin Gao ◽  
Zhejun Liu ◽  
Ye Wang ◽  
...  
Keyword(s):  
Inertial Sensors ◽  
Inertial Sensor ◽  
Low Cost ◽  
Satellite Navigation ◽  
Micro Electro Mechanical System ◽  
Indoor Navigation ◽  
Indoor Environments ◽  
Integrated Navigation ◽  
Parking Lots ◽  
Positioning Algorithm

Vehicles have to rely on satellite navigation in an open environment. However, satellite navigation cannot obtain accurate positioning information for vehicles in the interior of underground parking lots, as they comprise a semi-enclosed navigation space. Therefore, vehicular navigation needs to take into consideration both outdoor and indoor environments. Actually, outdoor navigation and indoor navigation require different positioning methods, and it is of great importance to choose a reasonable navigation and positioning algorithm solution for vehicles. Fortunately, the integrated navigation of the Global Positioning System (GPS) and the Micro-Electro-Mechanical System (MEMS) inertial navigation system could solve the problem of switching navigation algorithms in the entrance and exit of underground parking lots. This paper proposes a low cost vehicular seamless navigation technology based on the reduced inertial sensor system (RISS)/GPS between the outdoors and an underground garage. Specifically, the enhanced RISS is a positioning algorithm based on three inertial sensors and one odometer, which could achieve a similar location effect as the full model integrated navigation, reduce the costs greatly, and improve the efficiency of each sensor.

A Low-Cost MEMS Implementation Based on Sensor Fusion Algorithms

2015 ◽  
Vol 738-739 ◽  
pp. 42-45
Author(s):  
Xian Wei Wang ◽  
Jun Hai Jiang
Keyword(s):  
Kalman Filter ◽  
Sensor Fusion ◽  
Output Signal ◽  
Inertial Measurement Unit ◽  
Degrees Of Freedom ◽  
Low Cost ◽  
Micro Electro Mechanical System ◽  
Measurement Unit ◽  
Inertial Measurement ◽  
6 Degrees Of Freedom

In this paper a low-cost Micro-Electro-Mechanical System (MEMS) inertial measurement unit is designed, a 3-axis accelerometer and 3-axis gyroscope simulated 6 degrees of freedom orientation sensing through sensor fusion. By analyzing a simple complimentary filter and a more complex Kalman filter, the outputs of each sensor were combined and took advantage of the benefits of both sensors to improved results. The experimental results demonstrate that the output signal can be corrected suitability by means of the proposed method.

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