Heading estimation fusing inertial sensors and landmarks for indoor navigation using a smartphone in the pocket

The development of smartphone Micro-Electro-Mechanical Systems (MEMS) inertial sensors has provided opportunities to improve indoor navigation and positioning for location-based services. One area of indoor navigation research uses pedestrian dead reckoning (PDR) technology, in which the mobile phone must typically be held to the pedestrian’s chest. In this paper, we consider navigation in three other mobile phone carrying modes: “calling,” “pocket,” and “swinging.” For the calling mode, in which the pedestrian holds the phone to their face, the rotation matrix method is used to convert the phone’s gyroscope data from the calling state to the holding state, allowing calculation of the stable pedestrian forward direction. For a phone carried in a pedestrian’s trouser pocket, a heading complementary equation is established based on principal component analysis and rotation approach methods. In this case, the pedestrian heading is calculated by determining a subset of data that avoid 180° directional ambiguity and improve the heading accuracy. For the swinging mode, a heading capture method is used to obtain the heading of the lowest point of the pedestrian’s arm swing as they hold the phone. The direction of travel is then determined by successively adding the heading offsets each time the arm droops. Experimental analysis shows that 95% of the heading errors of the above three methods are less than 5.81°, 10.73°, and 9.22°, respectively. These results present better heading accuracy and reliability.

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Gravity-Based Methods for Heading Computation in Pedestrian Dead Reckoning

Sensors ◽

10.3390/s19051170 ◽

2019 ◽

Vol 19 (5) ◽

pp. 1170 ◽

Cited By ~ 3

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.

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Robust Heading Estimation for Indoor Pedestrian Navigation Using Unconstrained Smartphones

Wireless Communications and Mobile Computing ◽

10.1155/2018/5607036 ◽

2018 ◽

Vol 2018 ◽

pp. 1-11 ◽

Cited By ~ 3

Author(s):

Zhian Deng ◽

Xin Liu ◽

Zhiyu Qu ◽

Changbo Hou ◽

Weijian Si

Keyword(s):

Inertial Sensors ◽

Body Motion ◽

Hand Movements ◽

Estimation Errors ◽

Daily Lives ◽

Filtering Algorithm ◽

Pedestrian Navigation ◽

Heading Estimation ◽

Estimation System ◽

Previous User

Heading estimation using inertial sensors built-in smartphones has been considered as a central problem for indoor pedestrian navigation. For practical daily lives, it is necessary for heading estimation to allow an unconstrained use of smartphones, which means the varying device carrying positions and orientations. As a result, three special human body motion states, namely, random hand movements, carrying position transitions, and user turns, are introduced. However, most existing heading estimation approaches neglect the three motion states, which may render large estimation errors. We propose a robust heading estimation system adapting to the unconstrained use of smartphones. A novel detection and classification method is developed to detect the three motion states timely and discriminate them accurately. For normal working, the user heading is estimated by a PCA-based approach. If a user turn occurs, it is estimated by adding horizontal heading change to previous user heading directly. If one of the other two motion states occurs, it is obtained by averaging estimation results of the adjacent normal walking steps. Finally, an outlier filtering algorithm is developed to smooth the estimation results. Experimental results show that our approach is capable of handling the unconstrained situation of smartphones and outperforms previous approaches in terms of accuracy and applicability.

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Magnetic Field Gradient-Based EKF for Velocity Estimation in Indoor Navigation

Sensors ◽

10.3390/s20205726 ◽

2020 ◽

Vol 20 (20) ◽

pp. 5726

Author(s):

Makia Zmitri ◽

Hassen Fourati ◽

Christophe Prieur

Keyword(s):

Magnetic Field ◽

Field Gradient ◽

Inertial Sensors ◽

Magnetic Field Gradient ◽

Position Estimation ◽

Indoor Navigation ◽

Velocity Estimation ◽

Gradient Based ◽

Estimation Scheme ◽

The Magnetic Field

This paper proposes an advanced solution to improve the inertial velocity estimation of a rigid body, for indoor navigation, through implementing a magnetic field gradient-based Extended Kalman Filter (EKF). The proposed estimation scheme considers a set of data from a triad of inertial sensors (accelerometer and gyroscope), as well as a determined arrangement of magnetometers array. The inputs for the estimation scheme are the spatial derivatives of the magnetic field, from the magnetometers array, and the attitude, from the inertial sensors. As shown in the literature, there is a strong relation between the velocity and the measured magnetic field gradient. However, the latter usually suffers from high noises. Then, the novelty of the proposed EKF is to develop a specific equation to describe the dynamics of the magnetic field gradient. This contribution helps to filter, first, the magnetic field and its gradient and second, to better estimate the inertial velocity. Some numerical simulations that are based on an open source database show the targeted improvements. At the end of the paper, this approach is extended to position estimation in the case of a foot-mounted application and the results are very promising.

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Real-time location estimation for indoor navigation using a visual-inertial sensor

Sensor Review ◽

10.1108/sr-01-2020-0014 ◽

2020 ◽

Vol 40 (4) ◽

pp. 455-464

Author(s):

Zhe Wang ◽

Xisheng Li ◽

Xiaojuan Zhang ◽

Yanru Bai ◽

Chengcai Zheng

Keyword(s):

Real Time ◽

Inertial Sensors ◽

Inertial Sensor ◽

Detection Algorithm ◽

Indoor Navigation ◽

Location Estimation ◽

Evaluation Function ◽

Current Frame ◽

Content Type ◽

Adjacent Frame

Purpose The purpose of this study is to use visual and inertial sensors to achieve real-time location. How to provide an accurate location has become a popular research topic in the field of indoor navigation. Although the complementarity of vision and inertia has been widely applied in indoor navigation, many problems remain, such as inertial sensor deviation calibration, unsynchronized visual and inertial data acquisition and large amount of stored data. Design/methodology/approach First, this study demonstrates that the vanishing point (VP) evaluation function improves the precision of extraction, and the nearest ground corner point (NGCP) of the adjacent frame is estimated by pre-integrating the inertial sensor. The Sequential Similarity Detection Algorithm (SSDA) and Random Sample Consensus (RANSAC) algorithms are adopted to accurately match the adjacent NGCP in the estimated region of interest. Second, the model of visual pose is established by using the parameters of the camera itself, VP and NGCP. The model of inertial pose is established by pre-integrating. Third, location is calculated by fusing the model of vision and inertia. Findings In this paper, a novel method is proposed to fuse visual and inertial sensor to locate indoor environment. The authors describe the building of an embedded hardware platform to the best of their knowledge and compare the result with a mature method and POSAV310. Originality/value This paper proposes a VP evaluation function that is used to extract the most advantages in the intersection of a plurality of parallel lines. To improve the extraction speed of adjacent frame, the authors first proposed fusing the NGCP of the current frame and the calibrated pre-integration to estimate the NGCP of the next frame. The visual pose model was established using extinction VP and NGCP, calibration of inertial sensor. This theory offers the linear processing equation of gyroscope and accelerometer by the model of visual and inertial pose.

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

International Journal of Navigation and Observation ◽

10.1155/2012/753206 ◽

2012 ◽

Vol 2012 ◽

pp. 1-12 ◽

Cited By ~ 7

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.

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An Indoor Navigation System Based on Stereo Camera and Inertial Sensors with Points and Lines

Journal of Sensors ◽

10.1155/2018/4801584 ◽

2018 ◽

Vol 2018 ◽

pp. 1-14 ◽

Cited By ~ 1

Author(s):

Bo Yang ◽

Xiaosu Xu ◽

Tao Zhang ◽

Yao Li ◽

Jinwu Tong

Keyword(s):

Navigation System ◽

Inertial Sensors ◽

Indoor Navigation ◽

Measurement Unit ◽

Indoor Environments ◽

Complex Environments ◽

Stereo Camera ◽

Tightly Coupled ◽

Indoor Navigation System ◽

Better Than

An indoor navigation system based on stereo camera and inertial sensors with points and lines is proposed to further improve the accuracy and robustness of the navigation system in complex indoor environments. The point and line features, which are fast extracted by ORB method and line segment detector (LSD) method, are both employed in this system to improve its ability to adapt to complex environments. In addition, two different representations of lines are adopted to improve the efficiency of the system. Besides stereo camera, an inertial measurement unit (IMU) is also used in the system to further improve its accuracy and robustness. An estimator is designed to integrate the camera and IMU measurements in a tightly coupled approach. The experimental results show that the performance of the proposed navigation system is better than the point-only VINS and the vision-only navigation system with points and lines.

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Drift Control of Pedestrian Dead Reckoning (PDR) for Long Period Navigation under Different Smartphone Poses

Engineering Proceedings ◽

10.3390/ecsa-8-11302 ◽

2021 ◽

Vol 10 (1) ◽

pp. 21

Author(s):

Ahmed Mansour ◽

Wu Chen ◽

Huan Luo ◽

Yaxin Li ◽

Jingxian Wang ◽

...

Keyword(s):

Inertial Sensors ◽

Low Cost ◽

Dead Reckoning ◽

Field Measurements ◽

Step Length ◽

Detection Algorithm ◽

Long Period ◽

Walking Pattern ◽

Heading Estimation ◽

Pedestrian Dead Reckoning

The inherent errors of low-cost inertial sensors cause significant heading drift that accumulates over time, making it difficult to rely on Pedestrian Dead Reckoning (PDR) for navigation over a long period. Moreover, the flexible portability of the smartphone poses a challenge to PDR, especially for heading determination. In this work, we aimed to control the PDR drift under the conditions of the unconstrained smartphone to eventually enhance the PDR performance. To this end, we developed a robust step detection algorithm that efficiently captures the peak and valley events of the triggered steps regardless of the device’s pose. The correlation between these events was then leveraged as distinct features to improve smartphone pose detection. The proposed PDR system was then designed to select the step length and heading estimation approach based on a real-time walking pattern and pose discrimination algorithm. We also leveraged quasi-static magnetic field measurements that have less disturbance for estimating reliable compass heading and calibrating the gyro heading. Additionally, we also calibrated the step length and heading when a straight walking pattern is observed between two base nodes. Our results showed improved device pose recognition accuracy. Furthermore, robust and accurate results were achieved for step length, heading and position during long-term navigation under unconstrained smartphone conditions.

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Hybrid Onboard Smartphone Sensors Measurements to Improve Heading Estimation for Indoors Positioning Solutions

Kurdistan Journal of Applied Research ◽

10.24017/science.2019.2.5 ◽

2019 ◽

Vol 4 (2) ◽

pp. 50-60 ◽

Cited By ~ 1

Author(s):

Haval Darwesh Abdalkarim ◽

Halgurd Sarhang Maghdid

Keyword(s):

Angular Displacement ◽

Dead Reckoning ◽

Indoor Positioning ◽

Indoor Navigation ◽

Positioning Accuracy ◽

Position Information ◽

Precise Position ◽

Current Location ◽

Smartphone Sensors ◽

Heading Estimation

In the last decade, there is a significant progression and huge demand in using technology; specifically, those technologies are embedded in smartphones (SP). Examples of these technologies are embedding various sensors for multi-purposes. Positioning sensors (Accelerometer, Gyroscope, and Magnetometer) are one of the significant technologies. Besides this, indoor positioning services on smartphones are the main advantage of these sensors. There are many indoor positioning applications, for instance; billing, shopping, security and safety, indoor navigation, entertainment applications, and other point-of-interest (POI) applications. Nevertheless, precise position information through current positioning techniques is the main issue of these applications. The pedestrian dead reckoning (PDR) technique is one of the techniques in which the integration of onboard sensors is used for locating smartphones. Estimated distance, heading, and typical speed can be measured to determine the estimated position of the smartphone via using the PDR technique. The PDR technique offers a low positioning accuracy due to existing accumulated errors of the embedded sensors. To solve this issue, this article proposes a hybrid multi-sensors measurement to reduce the existing sensors drifts and errors and to increase estimated heading accuracy of the smartphone. Further, the sensors’ measurements with the previously estimated position are fused by using KALMAN Filter to determine the current location of the smartphone in each step of walking with better angular displacement accuracy. Proposed algorithm depends on increasing estimated angular displacement of the smartphone using combination of the integrated sensors’ measurements. The achieved positioning accuracy through the proposed approach and based on trial experiments is around 2 meters, which is equivalent to 10% improvement in comparison with state of the art.

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Mobile Robot Indoor Positioning Based on a Combination of Visual and Inertial Sensors

Sensors ◽

10.3390/s19081773 ◽

2019 ◽

Vol 19 (8) ◽

pp. 1773 ◽

Cited By ~ 7

Author(s):

Mingjing Gao ◽

Min Yu ◽

Hang Guo ◽

Yuan Xu

Keyword(s):

Mobile Robot ◽

Mobile Robots ◽

Inertial Sensors ◽

Inertial Sensor ◽

Indoor Positioning ◽

Indoor Navigation ◽

Depth Image ◽

Measurement Unit ◽

Translation Vector ◽

Visual Sensor

Multi-sensor integrated navigation technology has been applied to the indoor navigation and positioning of robots. For the problems of a low navigation accuracy and error accumulation, for mobile robots with a single sensor, an indoor mobile robot positioning method based on a visual and inertial sensor combination is presented in this paper. First, the visual sensor (Kinect) is used to obtain the color image and the depth image, and feature matching is performed by the improved scale-invariant feature transform (SIFT) algorithm. Then, the absolute orientation algorithm is used to calculate the rotation matrix and translation vector of a robot in two consecutive frames of images. An inertial measurement unit (IMU) has the advantages of high frequency updating and rapid, accurate positioning, and can compensate for the Kinect speed and lack of precision. Three-dimensional data, such as acceleration, angular velocity, magnetic field strength, and temperature data, can be obtained in real-time with an IMU. The data obtained by the visual sensor is loosely combined with that obtained by the IMU, that is, the differences in the positions and attitudes of the two sensor outputs are optimally combined by the adaptive fade-out extended Kalman filter to estimate the errors. Finally, several experiments show that this method can significantly improve the accuracy of the indoor positioning of the mobile robots based on the visual and inertial sensors.

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