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%.

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 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 Gravity-Based Methods for Heading Computation in Pedestrian Dead Reckoning

Sensors ◽  
2019 ◽  
Vol 19 (5) ◽  
pp. 1170 ◽  
Author(s):  
Adi Manos ◽  
Itzik Klein ◽  
Tamir Hazan
Keyword(s):  
Field Experiments ◽  
Dead Reckoning ◽  
Vertical Direction ◽  
Indoor Navigation ◽  
Magnetic Sensors ◽  
Dominant Factor ◽  
Heading Estimation ◽  
Using Data ◽  
Pedestrian Dead Reckoning ◽  
Gravity Direction

One of the common ways for solving indoor navigation is known as Pedestrian Dead Reckoning (PDR), which employs inertial and magnetic sensors typically embedded in a smartphone carried by a user. Estimation of the pedestrian’s heading is a crucial step in PDR algorithms, since it is a dominant factor in the positioning accuracy. In this paper, rather than assuming the device to be fixed in a certain orientation on the pedestrian, we focus on estimating the vertical direction in the sensor frame of an unconstrained smartphone. To that end, we establish a framework for gravity direction estimation and highlight the important role it has for solving the heading in the horizontal plane. Furthermore, we provide detailed derivation of several approaches for calculating the heading angle, based on either the gyroscope or the magnetic sensor, all of which employ the estimated vertical direction. These various methods—both for gravity direction and for heading estimation—are demonstrated, analyzed and compared using data recorded from field experiments with commercial smartphones.

scholarly journals Map Aided Pedestrian Dead Reckoning Using Buildings Information for Indoor Navigation Applications

Positioning ◽  
2013 ◽  
Vol 04 (03) ◽  
pp. 227-239 ◽  
Author(s):  
Mohamed Attia ◽  
Adel Moussa ◽  
Naser El-Sheimy
Keyword(s):  
Dead Reckoning ◽  
Indoor Navigation ◽  
Pedestrian Dead Reckoning

scholarly journals Daily Living Movement Recognition for Pedestrian Dead Reckoning Applications

2016 ◽  
Vol 2016 ◽  
pp. 1-13 ◽  
Author(s):  
Alessio Martinelli ◽  
Simone Morosi ◽  
Enrico Del Re
Keyword(s):  
Supervised Learning ◽  
Activity Recognition ◽  
Sport Activity ◽  
Dead Reckoning ◽  
Support Vector ◽  
Navigation Systems ◽  
Cyclic Movements ◽  
Movement Recognition ◽  
Definition Of ◽  
Pedestrian Dead Reckoning

Nowadays, activity recognition is a central topic in numerous applications such as patient and sport activity monitoring, surveillance, and navigation. By focusing on the latter, in particular Pedestrian Dead Reckoning navigation systems, activity recognition is generally exploited to get landmarks on the map of the buildings in order to permit the calibration of the navigation routines. The present work aims to provide a contribution to the definition of a more effective movement recognition for Pedestrian Dead Reckoning applications. The signal acquired by a belt-mounted triaxial accelerometer is considered as the input to the movement segmentation procedure which exploits Continuous Wavelet Transform to detect and segment cyclic movements such as walking. Furthermore, the segmented movements are provided to a supervised learning classifier in order to distinguish between activities such as walking and walking downstairs and upstairs. In particular, four supervised learning classification families are tested: decision tree, Support Vector Machine,k-nearest neighbour, and Ensemble Learner. Finally, the accuracy of the considered classification models is evaluated and the relative confusion matrices are presented.

scholarly journals Indoor Navigation Systems for Blind People

2018 ◽  
Author(s):  
Ali Hojjat
Keyword(s):  
Computer Vision ◽  
Visually Impaired ◽  
Indoor Navigation ◽  
Blind People ◽  
Navigation Systems ◽  
Visually Impaired People ◽  
Impaired People ◽  
Outdoor Environments

Indoor navigation systems must deal with absence of GPS signals, since they are only available in outdoor environments. Therefore, indoor systems have to rely upon other techniques for positioning users. Recently various indoor navigation systems have been designed and developed to help visually impaired people. In this paper an overview of some existing indoor navigation systems for visually impaired people are presented and they are compared from different perspectives. The evaluated techniques are ultrasonic systems, RFID-based solutions, computer vision aided navigation systems, ans smartphone-based applications.

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