Dead Reckoning System Research Articles

Wearable Inertial Sensors: Applications, Challenges, and Public Test Benches

The recent technological advances in sensor miniaturization and embedded processing have provided new challenges and possibilities to the field of wearable computing. Two research areas are particularly interested by this innovation: healthcare technology applications that are devoted to analyzing the daily activities of a person to evaluate their general health, and personal dead reckoning (PDR) systems that focus on the analysis of the person's movements to keep track of his/her position in dangerous environments and situations. The identification of suitable algorithms and techniques to process wearable sensors data is a research challenge that must be overcome for both areas. The possibility to compare different solutions over public test benches is crucial to this aim. For this reason, we present the human odometry outdoor data set (HOOD), a public data set for the PDR systems and the wearable human activity recognition folder (WHARF), a public repository for human activity recognition (HAR), composed of over 1,000 acceleration recordings referring to 14 daily activities, and a MATLAB library allowing the creation and validation of acceleration models of the activities.

Magicol: Indoor Localization Using Pervasive Magnetic Field and Opportunistic WiFi Sensing

Anomalies of the omnipresent earth magnetic (i.e., geomagnetic) field in an indoor environment, caused by local disturbances due to construction materials, give rise to noisy direction sensing that hinders any dead reckoning system. In this paper, we turn this unpalatable phenomenon into a favorable one. We present Magicol, an indoor localization and tracking system that embraces the local disturbances of the geomagnetic field. We tackle the low discernibility of the magnetic field by vectorizing consecutive magnetic signals on a per-step basis, and use vectors to shape the particle distribution in the estimation process. Magicol can also incorporate WiFi signals to achieve much improved positioning accuracy for indoor environments with WiFi infrastructure. We perform an in-depth study on the fusion of magnetic and WiFi signals. We design a two-pass bidirectional particle filtering process for maximum accuracy, and propose an on-demand WiFi scan strategy for energy savings. We further propose a compliant-walking method for location database construction that drastically simplifies the site survey effort. We conduct extensive experiments at representative indoor environments, including an office building, an underground parking garage, and a supermarket in which Magicol achieved a 90 percentile localization accuracy of 5 m, 1 m, and 8 m, respectively, using the magnetic field alone. The fusion with WiFi leads to 90 percentile accuracy of 3.5 m for localization and 0.9 m for tracking in the office environment. When using only the magnetism, Magicol consumes 9 × less energy in tracking compared to WiFi-based tracking.

Navigation of mobile robot using Low-cost GPS

A method of fusing data from a Global Positioning System (GPS) and a Dead Reckoning (DR) system for outdoor navigation of a Wheeled Mobile Robot (WMR) is proposed. The low-cost GPS receiver cannot be utilized by itself for navigating the mobile robot. Since, it produces an error of approximately 10–20 meters. DR can provide precise navigation data to the mobile robot but its errors accumulate over time. Since, all the previous data are utilized to calculate the current position information. DR needs to be complemented by other navigation sensors to navigate the mobile robot. The proposed GPS/DR data fusion method is based on the characteristics of the single GPS receiver output. The fused data provides accurate and robust navigation information to the outdoor mobile robot. Simulations were conducted using real GPS data which were then compared with the results using a Kalman filter that verified the potential of the proposed GPS/DR data fusion method.

Research of Autonomous Navigation Strategy for an Outdoor Mobile Robot

A kind of autonomous navigation strategy for an outdoor mobile robot is proposed in the real world environment. Encoder disk, electronic compass and GPS are used in the dead reckoning system of outdoor mobile robot. To reduce dead reckoning error, the extend Kalman filter method is adopted to realize data fusion from encoder disk and electronic compass. GPS receiver is used to correct the dead reckoning data to eliminate the accumulation error of the dead reckoning. Laser range finder is used to detect the obstacle and establish the environment map. Filed D* algorithm and DWA method are used to realize the obstacle avoidance and path planning. The experimental results show that the outdoor mobile robot can realize the autonomous navigation under the complex road condition.

An GPS/DR navigation system using neural network for mobile robot

Dead reckoning (DR) is frequently used for mobile robot navigation as it can provide precise short term navigation information but the errors of a DR system can accumulate over time. A global positioning system (GPS) can be used for outside navigation and localization but the error of a single GPS receiver is still big even though an error intentionally introduced into the system called the selective availability policy (SA) was already removed. Standard differential GPS (DGPS) can provide an accuracy of less than one meter but it is too expensive for the mass market aside from the need of having a base station to provide differential data. This paper proposes a new GPS/DR fusion method based on the data characteristics of a cheap single GPS receiver and use neural network to estimate the output of the GPS receiver to provide precise navigation information to the mobile robot. Simulation results validated the performance of the proposed method and showed its potential use in outdoor mobile robot navigation.

Intelligent Fusion of Wi-Fi and Inertial Sensor-Based Positioning Systems for Indoor Pedestrian Navigation

Indoor positioning systems based on wireless local area networks are growing rapidly in importance and gaining commercial interest. Pedestrian dead reckoning (PDR) systems, which rely on inertial sensors, such as accelerometers, gyroscopes, or even magnetometers to estimate users' movement, have also been widely adopted for real-time indoor pedestrian location tracking. Since both kinds of systems have their own advantages and disadvantages, a maximum likelihood-based fusion algorithm that integrates a typical Wi-Fi indoor positioning system with a PDR system is proposed in this paper. The strength of the PDR system should eliminate the weakness of the Wi-Fi positioning system and vice versa. The intelligent fusion algorithm can retrieve the initial user location and moving direction information without requiring any user intervention. Experimental results show that the proposed positioning system has better positioning accuracy than the PDR system or Wi-Fi positioning system alone.

A GPS/DR Data Fusion Method Based on the GPS Characteristics for Mobile Robot Navigation

In this paper we have considered the problem of outdoor mobile robot navigation using dead reckoning (DR) system and single GPS receiver. DR is a very simple and practical positioning technique. It is used in various positioning and navigation applications, especially for the mobile robot. DR can provide short term precise navigation information. But its errors will generally accumulate as the mobile robot continues to travel, and the calculated position of the mobile robot will become less and less accurate. For outdoor navigation application, GPS exhibit lots of advantages. It can provide real time and relatively accurate position data in spite of bad weather or other negative factors. But the big errors of civilian used single GPS receiver prevent it from applying to navigation for mobile robot alone. Differential GPS (DGPS) can be used to achieve an error of less than one meter but the costs are prohibitive in terms of commercializing it into the mass market. In this study, a cheap single GPS receiver cooperated with a DR system was used for the navigation system of a outdoors mobile robot in which a new GPS/DR data fusion method was utilized. This proposed fusion algorithm was based on the characteristics of the chosen single GPS receiver. The presented fusion algorithm does not bring much calculation burden and can provide accurate and robust navigation information for the mobile robot by adaptively switching between GPS/DR and DR when GPS lost the satellite signals. Simulation and experiment were performed to validate the effectiveness of the proposed fusion method and the good results showed its potential for outdoors mobile robot navigation.

Analytical and simulation models for collaborative localization

Collaborative localization is a special case for knowledge fusion where information is exchanged in order to attain improved global and local knowledge. We propose analytical as well as agent based simulation models for pedestrian dead reckoning (PDR) systems in agents collaborating to improve their location estimate by exchanging subjective position information when two agents are detected close to each other. The basis of improvement is the fact that two agents are at approximately the same position when they meet, and this can be used to update local position information. In analytical models we find that the localization error remains asymptotically finite in infinite systems or when there is at least one immobile agent (i.e. an agent with a zero localization error) in the system. In the agent model we tested finite systems under realistic (that is, inexact) meeting conditions and tested localization errors as function of several parameters. We found that a large finite system comprising hundreds of users is capable of collaborative localization with an essentially constant error under various conditions. The presented models can be used for predicting the improvement in localization that can be achieved by a collaboration among several mobile computers. Besides, our results can be considered as first steps toward a more general collaborative (incremental) form of knowledge fusion.

A Pedestrian Dead Reckoning System Integrating Low - Cost MEMS Inertial Sensors and GPS Receiver

The body-mounted inertial systems for pedestrian navigation do not require any preinstalled facilities and can run autonomously. The advantages over other technologies make it especially attractive for the applications such as first responders, military and consumer markets. The hardware platform integrating the low-cost, low-power and small-size MEMS (micro-electro-mechanical systems) inertial sensors and GPS (global positioning system) receiver is proposed. When the satellite signals are available, the location of the pedestrian is directly obtained from the GPS receiver. The inertial sensors are the complement of the GPS receiver in places where the GPS signals are not available, such as indoors, urban canyons and places under dense foliages. The height tracking is achieved by the barometer. The proposed PDR (pedestrian dead reckoning) algorithm is real-timely implemented in the platform. The simple but effective step detection and step length estimation method are realized to reduce the computation and memory requirements on the microprocessor. A complementary filter is proposed to fuse the data from the accelerometer, gyroscope and digital compass for decreasing the heading error, which is the main error source in positioning. The reliability and accuracy of the proposed system is verified by field pedestrian walking tests in outdoors and indoors. The positioning error is less than 4% of the total traveled distance. The results indicate that the pedestrian dead reckoning system is able to provide satisfactory tracking performance.

An Autonomous Waist - Mounted Pedestrian Dead Reckoning System by Coupling Low - Cost MEMS Inertial Sensors and GPS Receiver for 3D Urban Navigation

Global positioning system (GPS) offers a perfect solution to the 3-dimension(3D) navigation. However, the GPS-only solution can’t provide continuous and accurate position information in the unfavourable environments, such as urban canyons, indoor buildings, dense foliages due to signal blockage, interference, or jamming etc. A pedestrian dead reckoning (PDR) system integrating the self-contained inertial sensors with GPS receiver is proposed to provide a seamless outdoor/indoor 3D pedestrian navigation. The MEM sensor module attached to the user’s waist is composed of a 3-axis accelerometer, a 3-axis gyroscope, a 3-axis digital compass and a barometric pressure sensor, which doesn’t rely on any infrastructure. The positioning algorithm implements a loosely coupled GPS/PDR integration. The sensor data are fused via a complementary filter to reduce the integral drift and magnetic disturbance for accurate heading. The four key components of the PDR algorithm: step detection, stride length estimation, heading and position determination are described in detail and implemented by the microcontroller. The step is detected using the accelerometer signals by the combination of three approaches: sliding window, peak detection and zero-crossing. The step length is estimated using a simple linear relationship with the step frequency. By coupling the step length, azimuth and height, 3D navigation is achieved. The performance of the proposed system is carefully verified through several field outdoor and indoor walking tests. The positioning errors are below 3% of the total traveled distance. The main error source comes from the orientation estimation. The results indicate that the proposed system is effective in accurate tracking.

In-situ Bias Estimation of Low Grade Gyroscopes for Ship Launched Flight Vehicles

Abstract The paper presents mechanization of an analytical Transfer Alignment (TA) process and a novel scheme on in-situ bias estimation of Low Grade MEMS Gyroscopes based Inertial Navigation System. The objective of the work presented, is to devise a scheme for estimation of initial vector(s) i.e. velocity, attitude and biases of MEMS gyroscopes of inertial navigation system of flight vehicles being launched from moving ships. Inertial Navigation is a dead reckoning system and prior initialization of velocity and attitude is an essential requirement to achieve minimum Circular-Error-Probable (CEP) at the target point. Establishing the initial vector(s) is straight forward for the flight vehicles being launched from static launchers where the Gravity Vector and Earth Rate Vectors are being measured precisely and utilized for analytical Gyro-Compassing (GC) alignment. The same is not true for moving vehicles like ships, where the reference vectors like Earth Rate and Gravity are not known. Hence, an analytical Transfer Alignment (TA) scheme is developed for pre-initialization of Slave Inertial Navigation System (SINS) on-board flight vehicle with Master Inertial Navigation System (MINS) on-board moving ship. A 5–state Extended Kalman Filter based velocity matching technique is designed and an observability study is presented. The MEMS gyroscopes which have gained importance over their mechanical and optical counterparts are highly reliable, miniature in size and available at low cost. On contrary, these sensors have a poorer day-to-day bias repeatability of the order of 20°/hour which will result into degraded navigation performance. Hence, an attempt was made to devise a scheme to estimate the large biases of SINS, in-situ with external aid from MINS. The presented scheme estimates these gyro biases in tandem with the Transfer Alignment to an accuracy of 1.0°/hour, ensuring convergence within 3 minutes, without compromising on alignment accuracy. A suitable system configuration is conceived based on low grade MEMS gyroscopes and Quartz accelerometers. The total scheme is implemented in real time and experimental results are presented.

An indoor locationtracking system for smart parking

There are always frustrations for drivers in finding parking space and parking is costly in almost every major city in the world. In this paper, we propose a crowdsourcing solution by exploiting sensors in the smartphone to collect real-time parking availability information. In particular, we utilise a pedestrian dead reckoning (PDR) system to track the driver's trajectory to detect when he is about to leave his parking space. However, the effectiveness of a PDR system lies in its success in accurately estimating the user's moving distance and direction. In this work, we implement a waist-mounted-based PDR method on a smartphone that can measure the user's moving distance with a high accuracy. Furthermore, we design a map-matching algorithm to calibrate the direction errors from the gyro using building floor plans. The results of our experiment show that we can achieve about 98% accuracy in estimating the user's walking distance, with an overall location error of about 0.48 m.

Robot Navigation Using a DR/GPS Data Fusion

Dead Reckoning (DR) is one of the frequently used navigation system for a mobile robot. It provides short term navigation information but its error can accumulate over time without limit. The Global Positioning System (GPS) can be used for localization and navigation outdoors wherein the removal of the SA Policy improved the accuracy of the GPS for civilian use but the error is still quite large. Standard Differential GPS (DGPS) can be used to achieve an error of less than one meter but the costs are prohibitive in terms of commercializing it into the mass market. In this research study, a cheap GPS receiver was used for the navigation system of a mobile robot outdoors in which a new Kalman-filter based DR/GPS data fusion method was utilized. This proposed method is based on the characteristics of the GPS receiver. Fusing the data from the GPS receiver and the DR system provided precise navigation information for the mobile robot. Simulation was performed to check and validate the effectiveness of the proposed fusion method and good results showed its potential for mobile robot navigation outdoors.

Enhancing indoor inertial pedestrian navigation using a shoe-worn marker.

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.

Use of High Sensitivity GNSS Receiver Doppler Measurements for Indoor Pedestrian Dead Reckoning

Dead-reckoning (DR) algorithms, which use self-contained inertial sensors combined with gait analysis, have proven to be effective for pedestrian navigation purposes. In such DR systems, the primary error is often due to accumulated heading drifts. By tightly integrating global navigation satellite system (GNSS) Doppler measurements with DR, such accumulated heading errors can usually be accurately compensated. Under weak signal conditions, high sensitivity GNSS (HSGNSS) receivers with block processing techniques are often used, however, the Doppler quality of such receivers is relatively poor due to multipath, fading and signal attenuation. This often limits the benefits of integrating HSGNSS Doppler with DR. This paper investigates the benefits of using Doppler measurements from a novel direct vector HSGNSS receiver with pedestrian dead-reckoning (PDR) for indoor navigation. An indoor signal and multipath model is introduced which explains how conventional HSGNSS Doppler measurements are affected by indoor multipath. Velocity and Doppler estimated by using direct vector receivers are introduced and discussed. Real experimental data is processed and analyzed to assess the veracity of proposed method. It is shown when integrating HSGNSS Doppler with PDR algorithm, the proposed direct vector method are more helpful than conventional block processing method for the indoor environments considered herein.

A Survey of Indoor Inertial Positioning Systems for Pedestrians

With the continual miniaturisation of sensors and processing nodes, Pedestrian Dead Reckoning (PDR) systems are becoming feasible options for indoor tracking. These use inertial and other sensors, often combined with domain-specific knowledge about walking, to track user movements. There is currently a wealth of relevant literature spread across different research communities. In this survey, a taxonomy of modern PDRs is developed and used to contextualise the contributions from different areas. Techniques for step detection, characterisation, inertial navigation and step-and-heading-based dead-reckoning are reviewed and compared. Techniques that incorporate building maps through particle filters are analysed, along with hybrid systems that use absolute position fixes to correct dead-reckoning output. In addition, consideration is given to the possibility of using smartphones as PDR sensing devices. The survey concludes that PDR techniques alone can offer good short- to medium- term tracking under certain circumstances, but that regular absolute position fixes from partner systems will be needed to ensure long-term operation and to cope with unexpected behaviours. It concludes by identifying a detailed list of challenges for PDR researchers.

A Novel Dynamic Approach of Accelerometer Calibration in GPS/DR Integrated Navigation System

In this paper, a novel GPS (Global Positioning System) and DR (Dead Reckoning) system which is based on the accelerometer and gyroscope integrated system is designed and implemented, a new accelerometer calibration algorithm is also proposed. In this system, the odometer used in traditional DR system is replaced by a MEMS tri-axis accelerometer in order to decrease the cost and the volume of the system. The system is integrated by the Kalman filter and a new mathematical model is introduced. In the purpose of reducing the effect of the accumulated error caused by drift and bias of accelerometer, the accelerometer is calibrated online when GPS performs well. In this way, the integrated system does not only could obtain the high-precision positioning in real time, but also performs stably in practice.

A Ship GPS/DR Integrated Navigation System

The development of the global economy stimulates the exploitation of the ocean. A precise and stable ship navigation system is very important for people to explore the ocean. Dead reckoning (DR) system is a frequently used navigation system for sailing in the ocean. It can provide precise short term navigation information but the error of DR system can accumulate over time without limitation. GPS can be used for localization and navigation in outside environment. Although the SA policy was removed the accuracy of GPS for civilian use is still big. But the errors of GPS are bounded. Since the complementarity of DR and GPS system the integrated GPS/DR system can provide good navigation results. In this paper a new Kalman filter based DR/GPS data fusion method is proposed. This method is designed based on the characteristic of the GPS receiver. By using this data fusion method the cheap GPS receiver can cooperate with DR system to provide precise navigation information for ships. Simulation is conducted to validate the proposed fusion method. The good result shows the potential of this fusion method for the ship navigation.

Adaptive integrated navigation filtering based on accelerometer calibration

In this paper, a novel GPS (Global Positioning System) and DR (Dead Reckoning) system which was based on the accelerometer and gyroscope integrated system was designed and implemented. In this system, the odometer used in traditional DR system was replaced by a MEMS tri-axis accelerometer in order to decrease the cost and the volume of the system. The system was integrated by the Kalman filter and a new mathematical model was introduced. In order to reasonably use the GPS information, an adaptive algorithm based on single measurement system which could estimate the measurement noise covariance was obtained. On the purpose of reducing the effect of the accumulated error caused by drift and bias of accelerometer, the accelerometer was calibrated online when GPS performed well. In this way, the integrated system could not only obtain the high-precision positioning in real time, but also perform stably in practice. DOI: http://dx.doi.org/10.11591/telkomnika.v10i7.1603

Modified RHKF Filter for Improved DR/GPS Navigation against Uncertain Model Dynamics

In this paper, an error compensation technique for a dead reckoning (DR) system using a magnetic compass module is proposed. The magnetic compass-based azimuth may include a bias that varies with location due to the surrounding magnetic sources. In this paper, the DR system is integrated with a Global Positioning System (GPS) receiver using a finite impulse response (FIR) filter to reduce errors. This filter can estimate the varying bias more effectively than the conventional Kalman filter, which has an infinite impulse response structure. Moreover, the conventional receding horizon Kalman FIR (RHKF) filter is modified for application in nonlinear systems and to compensate the drawbacks of the RHKF filter. The modified RHKF filter is a novel RHKF filter scheme for nonlinear dynamics. The inverse covariance form of the linearized Kalman filter is combined with a receding horizon FIR strategy. This filter is then combined with an extended Kalman filter to enhance the convergence characteristics of the FIR filter. Also, the receding interval is extended to reduce the computational burden. The performance of the proposed DR/GPS integrated system using the modified RHKF filter is evaluated through simulation.