Dead Reckoning Research Articles

A fusion method of pedestrian dead reckoning and pseudo indoor plan based on conditional random field

Pedestrian dead reckoning (PDR), as an indoor positioning technology that can locate pedestrians only by terminal devices, has attracted more attention because of its convenience. However, there are several problems, i.e., cumulative error and uncertain initial position, in the practical application of PDR. To solve these problems, we propose a fusion method of PDR and pseudo indoor plan based on conditional random field (CRF), briefly as CRF-PDR. Firstly, CRF-PDR uses landmarks (i.e., the key indoor positions such as stairs and elevators) to build pseudo indoor plan, adopts random forest to identify the pedestrian’s activity at landmarks, and records these activities to form an activity sequence. Secondly, CRF modeling is used to accurately estimate the optimal landmark sequence passed by pedestrian with the help of pseudo indoor plan, so as to correct the position of PDR. Experimental results show that CRF-PDR can resolve the problems of cumulative error and uncertain initial position of PDR.

Upper Limb Position Tracking with a Single Inertial Sensor Using Dead Reckoning Method with Drift Correction Techniques.

Inertial sensors are widely used in human motion monitoring. Orientation and position are the two most widely used measurements for motion monitoring. Tracking with the use of multiple inertial sensors is based on kinematic modelling which achieves a good level of accuracy when biomechanical constraints are applied. More recently, there is growing interest in tracking motion with a single inertial sensor to simplify the measurement system. The dead reckoning method is commonly used for estimating position from inertial sensors. However, significant errors are generated after applying the dead reckoning method because of the presence of sensor offsets and drift. These errors limit the feasibility of monitoring upper limb motion via a single inertial sensing system. In this paper, error correction methods are evaluated to investigate the feasibility of using a single sensor to track the movement of one upper limb segment. These include zero velocity update, wavelet analysis and high-pass filtering. The experiments were carried out using the nine-hole peg test. The results show that zero velocity update is the most effective method to correct the drift from the dead reckoning-based position tracking. If this method is used, then the use of a single inertial sensor to track the movement of a single limb segment is feasible.

The Rescuer’s Navigation in Metro Stations Based on Inertial Sensors and WiFi

The demand for metro station rescue navigation is increasing. This paper presents an improved particle filter to challenge the navigation problem in metro stations. A particle filter is often used to estimate the position of pedestrians. However, the particle-impoverishment problem is inevitable. To solve this problem, a dingo optimization algorithm (DOA) with global search ability is introduced, and an improved particle filter called a dingo particle filter (DPF) is proposed. Dead reckoning (DR) is taken as the system equation, and WiFi matching results are used as the observation equation. The improved particle filter algorithm introduces a dingo optimization algorithm to improve the diversity of particles and effectively reduce the particle-impoverishment problem. The experimental results show that the average positioning accuracy is 1.1 m and 1.2 m.

Design and Experiments of Autonomous Path Tracking Based on Dead Reckoning

Path tracking is an important component of autonomous driving and most current path tracking research is based on different positioning sensors, such as GPS, cameras, and LIDAR. However, in certain extreme cases (e.g., in tunnels or indoor parking lots), if these sensors are unavailable, achieving accurate path tracking remains a problem that is worthy of study. This paper addresses this problem by designing a dead reckoning method that is solely reliant on wheel speed for localization. Specifically, a differential drive model is first used for estimating the current relative vehicle position in real time by rear wheel speed, and the deviation between the current path and the reference path is then calculated using the pure pursuit algorithm as a means of obtaining the target steering wheel angle and vehicle speed. The steering wheel and vehicle speed signals are then output by two PID controllers in order to control the vehicle, and the automatic driving path tracking is ultimately realized. Through exhaustive tests and experiments, the stop position error and tracking process error are compared under different conditions, and the effects of vehicle speed, look-ahead distance, starting position angle, and driving mode on tracking accuracy are analyzed. The experimental results show the average error of the end position to be 0.26 m, 0.383 m, and 0.505 m when using BMW-i3 to drive one lap automatically at speeds of 5 km/h, 10 km/h, and 15 km/h in a test area with a perimeter of approximately 200 m.

Indoor Spatiotemporal Contact Analytics Using Landmark-Aided Pedestrian Dead Reckoning on Smartphones.

Due to the prevalence of COVID-19, providing safe environments and reducing the risks of virus exposure play pivotal roles in our daily lives. Contact tracing is a well-established and widely-used approach to track and suppress the spread of viruses. Most digital contact tracing systems can detect direct face-to-face contact based on estimated proximity, without quantifying the exposed virus concentration. In particular, they rarely allow for quantitative analysis of indirect environmental exposure due to virus survival time in the air and constant airborne transmission. In this work, we propose an indoor spatiotemporal contact awareness framework (iSTCA), which explicitly considers the self-containing quantitative contact analytics approach with spatiotemporal information to provide accurate awareness of the virus quanta concentration in different origins at various times. Smartphone-based pedestrian dead reckoning (PDR) is employed to precisely detect the locations and trajectories for distance estimation and time assessment without the need to deploy extra infrastructure. The PDR technique we employ calibrates the accumulative error by identifying spatial landmarks automatically. We utilized a custom deep learning model composed of bidirectional long short-term memory (Bi-LSTM) and multi-head convolutional neural networks (CNNs) for extracting the local correlation and long-term dependency to recognize landmarks. By considering the spatial distance and time difference in an integrated manner, we can quantify the virus quanta concentration of the entire indoor environment at any time with all contributed virus particles. We conducted an extensive experiment based on practical scenarios to evaluate the performance of the proposed system, showing that the average positioning error is reduced to less than 0.7 m with high confidence and demonstrating the validity of our system for the virus quanta concentration quantification involving virus movement in a complex indoor environment.

GPS-assisted Indoor Pedestrian Dead Reckoning

Indoor pedestrian dead reckoning (PDR) using embedded inertial sensors in smartphones has been actively studied in the ubicomp community. However, PDR relying only on inertial sensors suffers from the accumulation of errors from the sensors. Researchers have employed various indoor landmarks detectable by smartphone sensors such as magnetic fingerprints caused by elevators and Bluetooth signals from beacons with known coordinates to compensate for the errors. This study proposes a new type of indoor landmark that does not require additional device installation, e.g., beacons, and training data collection in a target environment, e.g., magnetic fingerprints, unlike existing landmarks. This study proposes the use of GPS signals received by a smartphone to correct the accumulated errors of the PDR. While it is impossible to locate the smartphone indoors using GPS satellites, the smartphone can receive signals at a window-side area through windows from satellites aligned with the orientation of the window normal. Based on this idea, we design a machine-learning-based module for detecting the proximity of a user to a window and the orientation of the window, which enables us to roughly determine the absolute coordinates of the smartphone and to correct the accumulated errors by referring to positions of window-side areas found in the floor plan of the environment. A key technical contribution of this study is designing the module, such that it can be trained based on data from environments other than the target environment yet work in any environment by extracting GPS-related information independent of wall orientation. We evaluated the effectiveness of the proposed method using sensor data collected in real environments.

A Context-Aware Smartphone-Based 3D Indoor Positioning Using Pedestrian Dead Reckoning.

The rise in location-based service (LBS) applications has increased the need for indoor positioning. Various methods are available for indoor positioning, among which pedestrian dead reckoning (PDR) requires no infrastructure. However, with this method, cumulative error increases over time. Moreover, the robustness of the PDR positioning depends on different pedestrian activities, walking speeds and pedestrian characteristics. This paper proposes the adaptive PDR method to overcome these problems by recognizing various phone-carrying modes, including texting, calling and swinging, as well as different pedestrian activities, including ascending and descending stairs and walking. Different walking speeds are also distinguished. By detecting changes in speed during walking, PDR positioning remains accurate and robust despite speed variations. Each motion state is also studied separately based on gender. Using the proposed classification approach consisting of SVM and DTree algorithms, different motion states and walking speeds are identified with an overall accuracy of 97.03% for women and 97.67% for men. The step detection and step length estimation model parameters are also adjusted based on each walking speed, gender and motion state. The relative error values of distance estimation of the proposed method for texting, calling and swinging are 0.87%, 0.66% and 0.92% for women and 1.14%, 0.92% and 0.76% for men, respectively. Accelerometer, gyroscope and magnetometer data are integrated with a GDA filter for heading estimation. Furthermore, pressure sensor measurements are used to detect surface transmission between different floors of a building. Finally, for three phone-carrying modes, including texting, calling and swinging, the mean absolute positioning errors of the proposed method on a trajectory of 159.2 m in a multi-story building are, respectively, 1.28 m, 0.98 m and 1.29 m for women and 1.26 m, 1.17 m and 1.25 m for men.

Self-Test and Self-Calibration of Digital Closed-Loop Accelerometers

For accelerometers targeted in inertial navigation field, the DC bias error is the most destructive system error, affecting the final precision of long-term dead reckoning. This paper proposes a novel self-test and self-calibration technique for canceling out the DC bias error of the digital closed-loop accelerometers. The self-test of system DC bias is realized by injecting a 1-Bit ΣΔ modulated digital excitation and measuring the second-order harmonic distortion. As illustrated, the second-order harmonic distortion is related to the servo position deviation of the MEMS sensing element, which is one of the main causes of system DC bias error. The automatic capacitance compensation is carried out based on the amplitude and phase information of the detected second-order harmonic distortion, which can dynamically calibrate out the DC bias error. Test results show that there exists a near-linearity relationship between the system DC bias error and the second-order harmonic distortion, which is consistent with the proposed theoretical deduction. Based on the proposed method, the system DC bias error is effectively reduced from 150 to 4 mg, and unaffected by external acceleration bias.

Deep-Learning-Based Step Detection and Step Length Estimation With a Handheld IMU

A popular approach to track the position of a user is to use an inertial measurement unit (IMU), which allows tracking a user with a pedestrian dead reckoning (PDR) system by estimating the length and heading of each step a user takes. Furthermore, a user’s steps together with their length can be used to detect anomalies in the gait due to, e.g., a gait disorder. To determine the length and/or heading of a step, we first need an algorithm that estimates the boundaries of a step, i.e., the start and end, in the IMU signals. In this article, we propose a deep-learning-based step detection algorithm that detects the start and end of a step using only data from a handheld accelerometer. In this algorithm, also, the relationship between the start and end of a step is used to improve performance. To evaluate the performance of the detector, in contrast to most previous works that only look at the total number of detected steps, we determine if the start and end of each step are detected at the correct instant. This resulted in a step detector with an f-score of 99.2% and 99.0% for, respectively, detecting the start and end of a step. After detecting the start and end of the steps, we use a long short-term memory (LSTM)/convolutional neural network (CNN)-based step length estimator, which resulted in a mean absolute error (mae) on the step length of 3.21 cm. Finally, we also determine the combined performance of the step detector and step length estimator, i.e., where the proposed step detector is used to extract the accelerometer fragments of a step instead of assuming that the boundaries are known. This resulted in an mae of 6.56 cm using the deep-learning-based step length estimator.

An Improved Pedestrian Navigation Method Based on the Combination of Indoor Map Assistance and Adaptive Particle Filter

At present, the traditional indoor pedestrian navigation methods mainly include pedestrian dead reckoning (PDR) and zero velocity update (ZUPT), but these methods have the problem of error divergence during long time navigation. To solve this problem, under the condition of not relying on the active sensing information, combined with the characteristics of particles “not going through the wall” in the indoor map building structure, an improved adaptive particle filter (PF) based on the particle “not going through the wall” method is proposed for pedestrian navigation in this paper. This method can restrain the error divergence of the navigation system for a long time. Compared to the traditional pedestrian navigation method, based on the combination of indoor map assistance (MA) and particle filter, a global search method based on indoor MA is used to solve the indoor positioning problem under the condition of the unknown initial position and heading. In order to solve the problem of low operation efficiency caused by the large number of particles in PF, a calculation method of adaptively adjusting the number of particles in the process of particle resampling is proposed. The results of the simulation data and actual test data show that the proposed indoor integrated positioning method can effectively suppress the error divergence problem of the navigation system. Under the condition that the total distance is more than 415.44 m in the indoor environment of about 2600 m2, the average error and the maximum error of the position are less than two meters relative to the reference point.

Three‐dimensional dead reckoning of wall‐climbing robot based on information fusion of compound extended Kalman filter

AbstractDead reckoning accuracy determines the operation quality of pipeline mobile robot to a certain extent. With the progress of the industry, the research on the dead reckoning system of pipeline robot is more and more in‐depth, but how to improve dead reckoning accuracy is still a difficult problem for pipeline mobile robot. In this paper, to overcome the sensor defects of the traditional dead reckoning system, an error backpropagation neural network (BPNN) is introduced to compensate the sensor error caused by static drift and poor dynamic response. To reduce the interference of noise information, the covariance matching technique based on the Adaptive Neuro‐Fuzzy Inference System is used to estimate the measurement noise information. And then the two are combined in the algorithm based on the extended Kalman Filter (EKF) framework for data fusion. An EKF algorithm based on adaptive neural fuzzy combined with BPNN is proposed. Finally, a complete three‐dimensional dead reckoning system is established by coordinate transformation. The motion experiment of the quadruped wall‐climbing robot in the pipeline proves that the proximity of the calculated trajectory is not more than 6.00%, and the lowest is 1.07%, which effectively improves the reliability, robustness, and positioning accuracy of the dead reckoning.

Adaptive Inertial Sensor-Based Step Length Estimation Model

Pedestrian dead reckoning (PDR) using inertial sensors has paved the way for developing several approaches to step length estimation. In particular, emerging step length estimation models are readily available to be utilized on smartphones, yet they are seldom formulated considering the kinematics of the human body during walking in combination with measured step lengths. We present a new step length estimation model based on the acceleration magnitude and step frequency inputs herein. Spatial positions of anatomical landmarks on the human body during walking, tracked by an optical measurement system, were utilized in the derivation process. We evaluated the performance of the proposed model using our publicly available dataset that includes measurements collected for two types of walking modes, i.e., walking on a treadmill and rectangular-shaped test polygon. The proposed model achieved an overall mean absolute error (MAE) of 5.64 cm on the treadmill and an overall mean walked distance error of 4.55% on the test polygon, outperforming all the models selected for the comparison. The proposed model was also least affected by walking speed and is unaffected by smartphone orientation. Due to its promising results and favorable characteristics, it could present an appealing alternative for step length estimation in PDR-based approaches.

Multi-Source Adaptive Selection and Fusion for Pedestrian Dead Reckoning

Accurate multi-source fusion is based on the reliability, quantity, and fusion mode of the sources. The problem of selecting the optimal set for participating in the fusion process is nondeterministic-polynomial-time-hard and is neither sub-modular nor super-modular. Furthermore, in the case of the Kalman filter (KF) fusion algorithm, accurate statistical characteristics of noise are difficult to obtain, and this leads to an unsatisfactory fusion result. To settle the referred cases, a distributed and adaptive weighted fusion algorithm based on KF has been proposed in this paper. In this method, on the basis of the pseudo prior probability of the estimated state of each source, the reliability of the sources is evaluated and the optimal set is selected on a certain threshold. Experiments were performed on multi-source pedestrian dead reckoning for verifying the proposed algorithm. The results obtained from these experiments indicate that the optimal set can be selected accurately with minimal computation, and the fusion error is reduced by 16.6% as compared to the corresponding value resulting from the algorithm without improvements. The proposed adaptive source reliability and fusion weight evaluation is effective against the varied-noise multi-source fusion system, and the fusion error caused by inaccurate statistical characteristics of the noise is reduced by the adaptive weight evaluation. The proposed algorithm exhibits good robustness, adaptability, and value on applications.

Inaudible Sounds From Appliances as Anchors: A New Signal of Opportunity for Indoor Localization

The indoor localization still has challenges when compromising accuracy, cost, scalability, and so on. In recent years, signals of opportunity have been widely used for low-cost and scalable indoor localization services, especially in complex and changeable environments. In this article, a new opportunistic signal for indoor localization is proposed using the inaudible sound pattern from the appliances, which has superiority in long-term existence, stability, high availability, and privacy protection. The short-time Fourier transformed feature is exploited with a random forest model to recognize the appliance, and a promised matching algorithm is proposed to avoid any false detection. The recognized appliances can be used as standalone location indicators or supplements, such as loop closure pairs and additional landmarks, to the existed indoor localization system. The experimental results show that the appliance recognition achieves a recall rate of 79.66% under 100% precision, and a 95.43% improvement of localization accuracy is obtained to the pedestrian dead reckoning system over a 621.58-m trajectory.

Smartphone-Based Pedestrian NLOS Positioning Based on Acoustics and IMU Parameter Estimation

This paper proposes an integrated positioning algorithm for mobile devices and achieves long-term and high-accuracy indoor pedestrian tracking under severe non-Line-of-Sight (NLOS) scenarios. The traditional fusion method for hand-held devices lacks zero-speed correction and cannot clear the accumulated error of the Pedestrian Dead Reckoning (PDR). Secondly, the PDR algorithm also requires user privacy data for high positioning accuracy. Hence, we propose a customized model with acoustic and PDR through self-updating parameters with two novel fusing strategies: Kalman Filter with Least-Square (KFLS) and Kalman Filter with Bayesian Parameter Estimation (KFBPE), which utilize numerical feedback and Bayesian distribution, respectively. Experiments with Huawei Mate 9 show that both methods above can effectively eliminate the outlier resulting from severe signal loss, regardless of hand-holding gestures, with no individual privacy data required. Extensive experimental results demonstrate that the proposed methods are more efficient for NLOS and perform much better than the baselines of traditional fusion frameworks like the standard Kalman Filter. KFBPE has a relatively smoother tracking result, which guarantees an average positioning accuracy of up to 25 cm under the circumstance of nearly thirty percent acoustic signal loss (or NLOS) at the same time.

Online calibrated, energy-aware and heading corrected pedestrian navigation with foot-mounted MARG sensors

The objective of this paper is to propose an energy-aware and gait corrected pedestrian dead reckoning (PDR) approach using foot-mounted magnetic, angular rate and gravity (MARG) sensors. Compared with existing algorithms of PDR, the proposed method aims to solve three main problems for real pedestrian applications. First, to avoid limitations of off-line calibration for personal step length parameters, we utilize the zero-velocity-update (ZUPT) aided pedestrian MARG performance to continuously compute one’s pose information. Meanwhile, it accumulates the moving distance for further estimation of one’s step length during the initialization process. Secondly, due to different pedestrian gaits implicating the heading deviation angle between one’s moving direction and heading, there are non-negligible impacts on pedestrian dead reckoning accuracy. The linear Kalman filter (KF) is used to recursively estimate the deviated heading during aforementioned initialization process. The third problem is related to the energy consumption. Following three aspects of adaptive energy saving work are devised: (i) energy-aware strategy for gyroscopes measurements acquisition is adopted to guarantee lower energy consumption. (ii) Modes switching mechanism of navigation computation is applied to the initialization and dead reckoning processes. (iii) De-sampling after initialization process has been invoked. Finally, real-world experiments are carried out to evaluate the performances of developed PDR system. The results show the efficiency of the suggested approach.

Combination of VSLAM and a Magnetic Fingerprint Map to Improve Accuracy of Indoor Positioning

With the continual advancement of positioning technology, people's use of mobile devices has increased substantially. The global navigation satellite system (GNSS) has improved outdoor positioning performance. However, it cannot effectively locate indoor users owing to signal masking effects. Common indoor positioning technologies include radio frequencies, image visions, and pedestrian dead reckoning. However, the advantages and disadvantages of each technology prevent a single indoor positioning technology from solving problems related to various environmental factors. In this study, a hybrid method was proposed to improve the accuracy of indoor positioning by combining visual simultaneous localization and mapping (VSLAM) with a magnetic fingerprint map. A smartphone was used as an experimental device, and a built-in camera and magnetic sensor were used to collect data on the characteristics of the indoor environment and to determine the effect of the magnetic field on the building structure. First, through the use of a preestablished indoor magnetic fingerprint map, the initial position was obtained using the weighted k-nearest neighbor matching method. Subsequently, combined with the VSLAM, the Oriented FAST and Rotated BRIEF (ORB) feature was used to calculate the indoor coordinates of a user. Finally, the optimal user's position was determined by employing loose coupling and coordinate constraints from a magnetic fingerprint map. The findings indicated that the indoor positioning accuracy could reach 0.5 to 0.7 m and that different brands and models of mobile devices could achieve the same accuracy.

Bluetooth, Floor-Plan, and Microelectromechanical Systems-Assisted Wide-Area Audio Indoor Localization System: Apply to Smartphones

During the past decade, there has been an increasing interest in the audio-based indoor localization approach, which has benefited from the high precision, high privacy, and high security of the passive time difference of arrival positioning. However, the limited bandwidth and complexity in real-time ID encoding and decoding make it difficult to achieve wide-area (>1500 m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) localization in off-the-shelf smartphones. Plenty of previous work applied to a single region localization only with a small range. In this article, a hybrid acoustic signal transmission architecture based on frequency division multiple access, time division multiple access, and space division multiple access is perfected to cover the acoustic signal to a wider range. With the prior information of the Bluetooth and floor-plan, smartphones estimate which cell themselves are located in accurately. An enhanced particle filter algorithm, along with the microelectromechanical systems-based pedestrian dead reckoning and the floor-plan restraint, was proposed to make the smartphones tracking smarter and more robust. The proposed prototype system provided submeter-level localization accuracy and stable 1-Hz update rate in several typical wide indoor environments with less infrastructure, which demonstrates some values of reference and application owing to the work of this article.

Deep-Learning-Based Inertial Odometry for Pedestrian Tracking Using Attention Mechanism and Res2Net Module

Pedestrian dead reckoning is a challenging task due to the low-cost inertial sensor error accumulation. Recent research has shown that deep learning methods can achieve impressive performance in handling this issue. In this letter, we propose inertial odometry using a deep-learning-based velocity estimation method. The deep neural network based on Res2Net modules and two convolutional block attention modules is leveraged to restore the potential connection between the horizontal velocity vector and raw inertial data from a smartphone. Our network is trained using only 50% of the public inertial odometry dataset (RoNIN) data. Then, it is validated on the RoNIN testing dataset and another public inertial odometry dataset (OXIOD). Compared with the traditional step-length and heading-system-based algorithm, our approach decreases the absolute translation error (ATE) by 76%–86%. In addition, compared with the state-of-the-art deep learning method (RoNIN), our method improves its ATE by 6%–31.4%.

IMPROVEMENT OF PEDESTRIAN DEAD RECKONING ALGORITHM FOR INDOOR POSITIONING BY USING STEP LENGTH ESTIMATION

Abstract. Pedestrian dead reckoning (PDR) can solve the position information by its inertial measurement unit (IMU), which is a method to achieve indoor autonomous positioning without deploying positioning base stations in advance. However, affected by the accumulated error, the positioning accuracy will decrease with the increase of the moving distance. To improve the indoor positioning accuracy, the step length estimation of PDR is improved. A PDR method combining the information on walking frequency, acceleration variance, and fixed step is proposed. Firstly, the pedestrian's walking steps are detected and estimated by the threshold peak method; secondly, the step frequency and acceleration variance are calculated, and the step length estimation model is fitted with the fixed step length of the pedestrian to estimate the step length of each step; then, the fusion algorithm of magnetometer, accelerometer, and gyroscope are used to estimate the heading; finally, combined with the step number, step length, and heading angle, the position information of walking is calculated by dead reckoning. The experimental results show that in the practical distance of 42.6 m, the accuracy of dead reckoning distance based on the combination of step frequency, acceleration variance, and fixed step length can reach 98.47%, which is better than the fixed step length and linear step length version, 5.09% higher than the linear step length and 0.35% higher than the fixed step length version.