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Enhancing Indoor Inertial Pedestrian Navigation Using a Shoe-Worn Marker  [PDF]
Mitja Placer,Stanislav Kova?i?
Sensors , 2013, DOI: 10.3390/s130809836
Abstract: We propose a novel hybrid inertial sensors-based indoor pedestrian dead reckoning system, aided by computer vision-derived position measurements. In contrast to prior vision-based or vision-aided solutions, where environmental markers were used—either deployed in known positions or extracted directly from it—we use a shoe-fixed marker, which serves as positional reference to an opposite shoe-mounted camera during foot swing, making our system self-contained. Position measurements can be therefore more reliably fed to a complementary unscented Kalman filter, enhancing the accuracy of the estimated travelled path for 78%, compared to using solely zero velocities as pseudo-measurements.
A Zero Velocity Detection Algorithm Using Inertial Sensors for Pedestrian Navigation Systems  [PDF]
Sang Kyeong Park,Young Soo Suh
Sensors , 2010, DOI: 10.3390/s101009163
Abstract: In pedestrian navigation systems, the position of a pedestrian is computed using an inertial navigation algorithm. In the algorithm, the zero velocity updating plays an important role, where zero velocity intervals are detected and the velocity error is reset. To use the zero velocity updating, it is necessary to detect zero velocity intervals reliably. A new zero detection algorithm is proposed in the paper, where only one gyroscope value is used. A Markov model is constructed using segmentation of gyroscope outputs instead of using gyroscope outputs directly, which makes the zero velocity detection more reliable.
Height Compensation Using Ground Inclination Estimation in Inertial Sensor-Based Pedestrian Navigation  [PDF]
Sang Kyeong Park,Young Soo Suh
Sensors , 2011, DOI: 10.3390/s110808045
Abstract: In an inertial sensor-based pedestrian navigation system, the position is estimated by double integrating external acceleration. A new algorithm is proposed to reduce z axis position (height) error. When a foot is on the ground, a foot angle is estimated using accelerometer output. Using a foot angle, the inclination angle of a road is estimated. Using this road inclination angle, height difference of one walking step is estimated and this estimation is used to reduce height error. Through walking experiments on roads with different inclination angles, the usefulness of the proposed algorithm is verified.
Enhancing Positioning Accuracy in Urban Terrain by Fusing Data from a GPS Receiver, Inertial Sensors, Stereo-Camera and Digital Maps for Pedestrian Navigation  [PDF]
Przemyslaw Baranski,Pawel Strumillo
Sensors , 2012, DOI: 10.3390/s120606764
Abstract: The paper presents an algorithm for estimating a pedestrian location in an urban environment. The algorithm is based on the particle filter and uses different data sources: a GPS receiver, inertial sensors, probability maps and a stereo camera. Inertial sensors are used to estimate a relative displacement of a pedestrian. A gyroscope estimates a change in the heading direction. An accelerometer is used to count a pedestrian’s steps and their lengths. The so-called probability maps help to limit GPS inaccuracy by imposing constraints on pedestrian kinematics, e.g., it is assumed that a pedestrian cannot cross buildings, fences etc. This limits position inaccuracy to ca. 10 m. Incorporation of depth estimates derived from a stereo camera that are compared to the 3D model of an environment has enabled further reduction of positioning errors. As a result, for 90% of the time, the algorithm is able to estimate a pedestrian location with an error smaller than 2 m, compared to an error of 6.5 m for a navigation based solely on GPS.
Online three-axis magnetometer calibration for a pedestrian navigation system using a foot-mounted inertial navigation system

- , 2016, DOI: 10.16511/j.cnki.qhdxxb.2016.22.011
Abstract: 基于低成本微电子机械系统(MEMS)惯性传感器的足绑式惯性导航系统(INS)是行人自主导航常用的一种方式, 足绑式INS可与磁强计组合来约束航向角误差, 但磁强计存在误差需要校准。该文提出了适合行人导航的磁强计误差模型和在线校准算法。根据磁强计的误差特性和足绑式INS的机动性, 建立了磁强计误差变量的状态方程和观测方程, 利用扩展Kalman滤波器(EKF)对三轴磁强计误差进行在线估计和实时校准, 利用零速修正(ZUPT)算法和磁航向角约束算法对足绑式INS的误差进行约束。为验证算法的有效性, 在操场进行了一圈徒步行走实验。实验结果表明: 使用磁强计误差在线辨识和校准算法后, 与未进行磁强计误差校准相比, 行人导航东向终点误差由-110.7 m减小到1.8 m, 北向终点误差由37.8 m减小到5.2 m, 磁强计误差得到有效校正。该算法实现了基于足绑式INS的行人导航磁强计误差在线校准, 大幅提高了行人自主导航的定位精度。
Abstract:Foot-mounted inertial navigation systems (INS) having inexpensive micro electro mechanical system (MEMS)-based inertial sensors are widely used for pedestrian navigation. A foot-mounted INS can be combined with a magnetometer to constrain the heading angle error, but the magnetometer needs to be calibrated before use. This paper presents a magnetometer error model and an online calibration algorithm based on the foot-mounted INS characteristics. The magnetometer error characteristics and the foot-mounted INS mobility characteristics are used to develop a state equation and a magnetometer error measurement equation. An extended Kalman filter (EKF) is used for the online estimation and real-time calibration of the three-axis magnetometer errors with the zero velocity update (ZUPT) algorithm and a magnetic heading angle constraint algorithm for error constraint. The algorithm is validated by walking in a square playing ground. The results show that the online estimation and calibration algorithm reduce the end position error of the east direction from -110.7 to 1.8 m and the end position error of the north direction from 37.8 to 5.2 m compared to the system without calibration. This algorithm provides online calibration of magnetometer errors and significantly improves the positioning accuracy of pedestrian navigation.
Advanced Integration of WiFi and Inertial Navigation Systems for Indoor Mobile Positioning  [cached]
Evennou Frédéric,Marx Fran?ois
EURASIP Journal on Advances in Signal Processing , 2006,
Abstract: This paper presents an aided dead-reckoning navigation structure and signal processing algorithms for self localization of an autonomous mobile device by fusing pedestrian dead reckoning and WiFi signal strength measurements. WiFi and inertial navigation systems (INS) are used for positioning and attitude determination in a wide range of applications. Over the last few years, a number of low-cost inertial sensors have become available. Although they exhibit large errors, WiFi measurements can be used to correct the drift weakening the navigation based on this technology. On the other hand, INS sensors can interact with the WiFi positioning system as they provide high-accuracy real-time navigation. A structure based on a Kalman filter and a particle filter is proposed. It fuses the heterogeneous information coming from those two independent technologies. Finally, the benefits of the proposed architecture are evaluated and compared with the pure WiFi and INS positioning systems.
Design and Implementation of an Inertial Navigation System for Pedestrians Based on a Low-Cost MEMS IMU  [PDF]
Francesco Montorsi,Fabrizio Pancaldi,Giorgio M. Vitetta
Computer Science , 2015,
Abstract: Inertial navigation systems for pedestrians are infrastructure-less and can achieve sub-meter accuracy in the short/medium period. However, when low-cost inertial measurement units (IMU) are employed for their implementation, they suffer from a slowly growing drift between the true pedestrian position and the corresponding estimated position. In this paper we illustrate a novel solution to mitigate such a drift by: a) using only accelerometer and gyroscope measurements (no magnetometers required); b) including the sensor error model parameters in the state vector of an extended Kalman filter; c) adopting a novel soft heuristic for foot stance detection and for zero-velocity updates. Experimental results evidence that our inertial-only navigation system can achieve similar or better performance with respect to pedestrian dead-reckoning systems presented in related studies, although the adopted IMU is less accurate than more expensive counterparts.
Map Aided Pedestrian Dead Reckoning Using Buildings Information for Indoor Navigation Applications  [PDF]
Mohamed Attia, Adel Moussa, Naser El-Sheimy
Positioning (POS) , 2013, DOI: 10.4236/pos.2013.43023
Abstract: Navigation systems play an important role in many vital disciplines. Determining the location of a user relative to its physical environment is an important part of many indoor-based navigation services such as user navigation, enhanced 911 (E911), law enforcement, location-based and marketing services. Indoor navigation applications require a reliable, trustful and continuous navigation solution that overcomes the challenge of Global Navigation Satellite System (GNSS) signal unavailability. To compensate for this issue, other navigation systems such as Inertial Navigation System (INS) are introduced, however, over time there is a significant amount of drift especially in common with low-cost commercial sensors. In this paper, a map aided navigation solution is developed. This research develops an aiding system that utilizes geospatial data to assist the navigation solution by providing virtual boundaries for the navigation trajectories and limits its possibilities only when it is logical to locate the user on a map. The algorithm develops a Pedestrian Dead Reckoning (PDR) based on smart-phone accelerometer and magnetometer sensors to provide the navigation solution. Geospatial model for two indoor environments with a developed map matching algorithm was used to match and project navigation position estimates on the geospatial map. The developed algorithms were field tested in indoor environments and yielded accurate matching results as well as a significant enhancement to positional accuracy. The achieved results demonstrate that the contribution of the developed map aided system enhances the reliability, usability, and accuracy of navigation trajectories in indoor environments.

Low-Cost MEMS-Based Pedestrian Navigation Technique for GPS-Denied Areas  [PDF]
Abdelrahman Ali,Naser El-Sheimy
Journal of Sensors , 2013, DOI: 10.1155/2013/197090
Abstract: The progress in the micro electro mechanical system (MEMS) sensors technology in size, cost, weight, and power consumption allows for new research opportunities in the navigation field. Today, most of smartphones, tablets, and other handheld devices are fully packed with the required sensors for any navigation system such as GPS, gyroscope, accelerometer, magnetometer, and pressure sensors. For seamless navigation, the sensors’ signal quality and the sensors availability are major challenges. Heading estimation is a fundamental challenge in the GPS-denied environments; therefore, targeting accurate attitude estimation is considered significant contribution to the overall navigation error. For that end, this research targets an improved pedestrian navigation by developing sensors fusion technique to exploit the gyroscope, magnetometer, and accelerometer data for device attitude estimation in the different environments based on quaternion mechanization. Results indicate that the improvement in the traveled distance and the heading estimations is capable of reducing the overall position error to be less than 15?m in the harsh environments. 1. Introduction Personal navigation requires technologies that are immune to signal obstructions and fading. One of the major challenges is obtaining a good heading solution in different environments and for different user positions without external absolute reference signals. Part of this challenge arises from the complexity and freedom of movement of a typical handheld user where the heading observability considerably degrades in low-speed walking, making this problem even more challenging. However, for short periods, the relative attitude and heading information is quite reliable. Self-contained systems requiring minimal infrastructure, for example, inertial measurement units (IMUs), stand as a viable option, since pedestrian navigation is not only focused on outdoor navigation but also on indoor navigation. Nowadays, most of the smartphones are programmable and equipped with self-contained, low cost, small size, and power-efficient sensors, such as magnetometers, gyroscopes, and accelerometers. Hence, integrating IMUs navigation solution with a magnetometer-based heading can play an important role in pedestrian navigation in all environments. In the current state of the art in MEMS technology, the accuracy of gyroscopes is not good enough for deriving an absolute heading or relative heading over longer durations of time. However, for short periods, the relative attitude information is quite reliable. Magnetometers,
Gradient Navigation Model for Pedestrian Dynamics  [PDF]
Felix Dietrich,Gerta K?ster
Physics , 2014, DOI: 10.1103/PhysRevE.89.062801
Abstract: We present a new microscopic ODE-based model for pedestrian dynamics: the Gradient Navigation Model. The model uses a superposition of gradients of distance functions to directly change the direction of the velocity vector. The velocity is then integrated to obtain the location. The approach differs fundamentally from force based models needing only three equations to derive the ODE system, as opposed to four in, e.g., the Social Force Model. Also, as a result, pedestrians are no longer subject to inertia. Several other advantages ensue: Model induced oscillations are avoided completely since no actual forces are present. The derivatives in the equations of motion are smooth and therefore allow the use of fast and accurate high order numerical integrators. At the same time, existence and uniqueness of the solution to the ODE system follow almost directly from the smoothness properties. In addition, we introduce a method to calibrate parameters by theoretical arguments based on empirically validated assumptions rather than by numerical tests. These parameters, combined with the accurate integration, yield simulation results with no collisions of pedestrians. Several empirically observed system phenomena emerge without the need to recalibrate the parameter set for each scenario: obstacle avoidance, lane formation, stop-and-go waves and congestion at bottlenecks. The density evolution in the latter is shown to be quantitatively close to controlled experiments. Likewise, we observe a dependence of the crowd velocity on the local density that compares well with benchmark fundamental diagrams.
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