Pedestrian Dead Reckoning Research Articles

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.

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%.

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.

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.

An extended usability and UX evaluation of a mobile application for the navigation of individuals with blindness and visual impairments indoors: An evaluation approach combined with training sessions

Navigating indoor spaces is especially challenging for individuals with blindness and visual impairments. Although many solutions currently exist, the acceptance of most of them is extremely poor due to their technical limitations and the complete lack of taking into consideration factors, such as usability and the perceived experience among others, which influence adoption rates. To alleviate this problem, we created BlindMuseumTourer, a state-of-the-art indoor navigation smartphone application that tracks and navigates the user inside the spaces of a museum. At the same time, it provides services for narration and description of the exhibits. The proposed system consists of an Android application that leverages the sensors found on smartphones and utilizes a novel pedestrian dead reckoning (PDR) mechanism that optionally takes input from the Bluetooth low energy (BLE) beacons specially mounted on the exhibits. This article presents the extended Usability and User Experience evaluation of BlindMuseumTourer and the findings carried out with 30 participants having varying degrees of blindness. Throughout this process, we received feedback for improving both the available functionality and the specialized user-centred training sessions in which blind users are first exposed to our application’s functionality. The methodology of this evaluation employs standardized questionnaires and semi-structured interviews, and the results indicate an overall positive attitude from the users. In the future, we intend to extend the number and type of indoor spaces supported by our application.

A Low-Cost Foot-Placed UWB and IMU Fusion-Based Indoor Pedestrian Tracking System for IoT Applications

Among existing wireless and wearable indoor pedestrian tracking solutions, the ultra-wideband (UWB) and inertial measurement unit (IMU) sensors are the popular options due to their accurate and globally referenced positioning, and low-cost and compact size, respectively. However, the UWB position accuracy is compromised by the indoor non-line of sight (NLOS) and the IMU estimation suffers from orientation drift as well as requiring position initialization. To overcome these limitations, this paper proposes a low-cost foot-placed UWB and IMU fusion-based indoor pedestrian tracking system. Our data fusion model is an improved loosely coupled Kalman filter with the inclusion of valid UWB observation detection. In this manner, the proposed system not only adjusts the consumer-grade IMU's accumulated drift but also filters out any NLOS instances in the UWB observation. We validated the performance of the proposed system with two experimental scenarios in a complex indoor environment. The root mean square (RMS) positioning accuracy of our data fusion model is enhanced by 60%, 53%, and 27% compared to that of the IMU-based pedestrian dead reckoning, raw UWB position, and conventional fusion model, respectively, in the single-lap NLOS scenario, and by 70%, 34%, and 12%, respectively, in the multi-lap LOS+NLOS scenario.

A PDR/WiFi Indoor Navigation Algorithm Using the Federated Particle Filter

This paper offers a solution to challenge navigation in the indoor environment by making use of the existing infrastructure. Estimating pedestrian trajectory using pedestrian dead reckoning (PDR) and WiFi is a very popular technique. However, cumulative errors and mismatching are major problems in PDR and WiFi fingerprint matching, respectively. PDR and pedestrian heading are used as the state transition equation, and the step length and WiFi matching results are used as observation equations. A federated particle filter (FPF) based on the principle of information sharing is proposed to fusion PDR and WiFi, which improves pedestrian navigation accuracy. The experimental results show that the average positioning accuracy is 0.94 m and 1.5 m, respectively.

A Continuous PDR and GNSS Fusing Algorithm for Smartphone Positioning

Pedestrian dead reckoning (PDR), used in state-of-the-art smartphones, calculates pedestrian positions by using built-in inertial sensors. However, the complex and changeable usage modes of smartphones have been obstructing the development of PDR in the field of gait detection. Since the measurement of the sensor is affected by noise, position errors will emerge, needing to be corrected periodically via external measurements. To this end, an optimization-based PDR (OBPDR) method for smartphones is proposed in this study. First, an improved finite state machine (IFSM) gait detection method is designed, which can improve the gait recognition rate and stability compared with the traditional peak detection method. Second, the step detection algorithm proposed in this paper is combined with a heading estimation to obtain the PDR dynamic model. Finally, the measurements of GNSS are fused to the PDR model, based on an adaptive extended Kalman filter (AEKF) algorithm, which can enhance the adaptability of the system to the environment in order to reduce the cumulative errors of PDR. Experiments are carried out to evaluate the performance of the proposed method. The results indicate that compared with the gait detection method, based on peak detection, and the integrated positioning method, based on an extended Kalman filter, the proposed method boasts favorable robustness and a high gait recognition rate, the recognition accuracy being kept between 97.5% and 98.5%; the average position error decreased by more than 67.25%.

Pedestrian Gait Information Aided Visual Inertial SLAM for Indoor Positioning Using Handheld Smartphones

Simultaneous localization and mapping (SLAM) is currently a widely used technology for indoor positioning. Many studies have focused on the smartphone-based localization and navigation for its portability and feature-rich sensors. But due to the low-quality sensors and complex environments, current SLAM-based positioning technology using smartphones still poses great challenges, such as inherent cumulative global drift and potential divergence facing texture-less indoor regions. For the regular gait characteristics of pedestrians when naturally walking, the gait motion model can certainly provide effective observation of the pedestrian motion state. We propose a visual-inertial odometry (VIO) assisted by pedestrian gait information for smartphone-based indoor positioning. This work mainly builds two additional state constraints, pedestrian velocity, and step displacement, obtained by the pedestrian dead reckoning (PDR) algorithm for the visual-inertial tracking system. For each step, the corresponding residual term of step length and velocity constraints is constructed and added to the cost function for nonlinear sliding-window optimization. Furthermore, the step displacement is applied again in the four-degree-of-freedom (4-DOF) graph-based optimization to refine the trajectory. VIO system also assists the PDR algorithm in mode switching, to improve the accuracy of gait information by applying the adaptive step length formula. Field experiments were conducted, and the results indicate that with the aiding of the pedestrian gait information, the accuracy and robustness of the visual-inertial pedestrian tracking system using smartphones have been significantly improved. Compared with the state-of-the-art algorithm monocular visual-inertial navigation system (VINS-MONO), our method improves the accuracy by 54.6% on average in our field tests in challenging environments.

Practical and Accurate Indoor Localization System Using Deep Learning.

Indoor localization is an important technology for providing various location-based services to smartphones. Among the various indoor localization technologies, pedestrian dead reckoning using inertial measurement units is a simple and highly practical solution for indoor localization. In this study, we propose a smartphone-based indoor localization system using pedestrian dead reckoning. To create a deep learning model for estimating the moving speed, accelerometer data and GPS values were used as input data and data labels, respectively. This is a practical solution compared with conventional indoor localization mechanisms using deep learning. We improved the positioning accuracy via data preprocessing, data augmentation, deep learning modeling, and correction of heading direction. In a horseshoe-shaped indoor building of 240 m in length, the experimental results show a distance error of approximately 3 to 5 m.

Sensor fusion for indoor positioning system through improved RSSI and PDR methods

Given the growth of the Internet of Things (IoT) and smart home appliances, the concept of the Indoor Positioning System or IPS has considerably risen. The major application of IPS is in locating people in roofed places. In this type of positioning, accuracy has always been the most important challenge. The main focus of this article is to improve the indoor positioning of people. First, two positioning approaches are proposed, Radio Signal Strength Indicator (RSSI) and Pedestrian Dead Reckoning (PDR). In the RSSI positioning part, the distance between the participating nodes is calculated based on the low-fluctuating values of RSSI. A novel filter called Weight-Based Optimization (WBO) is developed to optimize these raw RSSI values. Moreover, the path loss model parameters, which are location-dependent, are calculated by employing Particle Swarm Optimization (PSO) to convert the resulting RSSI values to distances. In the PDR positioning part, the accelerometer sensor data is used to detect the person’s steps, and the compass sensor data is used to detect the heading direction. Finally, the results of RSSI and PDR methods are combined using a sensor fusion approach. The positioning accuracy resulting from the proposed IPS approach is 68 cm, which is far better than the existing methods.

LiDR: Visible-Light-Communication-Assisted Dead Reckoning for Accurate Indoor Localization

Pedestrian dead reckoning (PDR) is an inertial navigation system that relies on smartphone sensors for estimating a pedestrian’s step movements. However, such systems suffer from poor accuracy due to the drift and inherent noise in sensor readings. In addition, step size variation among pedestrians and device heterogeneity pose further challenges for building a scalable PDR system that can provide uniform performance across various devices and a diverse range of users. Visible light positioning (VLP), which uses LED lights with visible light communication (VLC) capability to provide high-accuracy localization, can achieve precision of a few cm. However, VLP systems suffer from practical limitations due to occasional line-of-sight (LOS) blockage and the sparse density of lighting in large-scale indoor venues. In this work, we propose a light-assisted dead reckoning (LiDR) system, which aims to address the problems of both VLP and PDR. It uses LED lighting as high-accuracy location landmarks to provide regular calibration for the PDR and estimates the individual pedestrians’ step size for increased accuracy. In addition, a light-shape-based heading angle correction algorithm is proposed to reduce the heading angle error and further improve the accuracy. The system is implemented as an Android-based navigation application, with a digital map and cloud-based backend storage for location, device, and user-specific parameters. The real-time performance of the system is evaluated in a 450- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\text{m}^{2}$ </tex-math></inline-formula> lab and on a 150-m walking track. The experimental results demonstrate that with a maximum light spacing of 15 m, an overall average accuracy of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\lt ~0.7$ </tex-math></inline-formula> m can be achieved for the whole system.

FlexPDR: Fully Flexible Pedestrian Dead Reckoning Using Online Multimode Recognition and Time-Series Decomposition

Smartphone-based pedestrian dead reckoning (PDR) has been widely used indoors for continuous localization. However, the specific tracking solutions under different modes are vulnerable to mode transition and thus degrading performance. The robustness of pedestrian navigation may be weakened due to the mix of smartphone motions and walking patterns. Due to this challenge, most existing PDR methods assume that the smartphone is carried in a certain pose, such as handheld horizontally, swinging, calling, and pocketed, ignoring the short period but negative transition impact on tracking, which limits its flexibility when applying in the Internet of Things (IoT) services. To achieve a fully flexible PDR (i.e., FlexPDR), this article enhances the robustness and smoothness of pedestrian tracking during the transition between several phone poses and regular motion modes for the first time. We propose a Bayesian-based real-time multimode recognition method that does not require any posterior information, together with a time-series decomposition approach for adaptively tracking scheme-switching. The proposed FlexPDR system achieved a real-time smartphone indoor positioning with a high position accuracy of 98.11% on the specific situation that mixed mode-switching happens, which outperformed other state-of-the-art methods.

A Low-Cost and Efficient Indoor Fusion Localization Method.

Accurate indoor location information has considerable social and economic value in applications, such as pedestrian heatmapping and indoor navigation. Ultrasonic-based approaches have received significant attention mainly since they have advantages in terms of positioning with temporal correlation. However, it is a great challenge to gain accurate indoor localization due to complex indoor environments such as non-uniform indoor facilities. To address this problem, we propose a fusion localization method in the indoor environment that integrates the localization information of inertial sensors and acoustic signals. Meanwhile, the threshold scheme is used to eliminate outliers during the positioning process. In this paper, the estimated location is fused by the adaptive distance weight for the time difference of arrival (TDOA) estimation and improved pedestrian dead reckoning (PDR) estimation. Three experimental scenes have been developed. The experimental results demonstrate that the proposed method has higher localization accuracy in determining the pedestrian location than the state-of-the-art methods. It resolves the problem of outliers in indoor acoustic signal localization and cumulative errors in inertial sensors. The proposed method achieves better performance in the trade-off between localization accuracy and low cost.

CRCLoc: A Crowdsourcing-Based Radio Map Construction Method for WiFi Fingerprinting Localization

The WiFi-based fingerprint indoor-positioning system has attracted increasing interest from industry and academia, benefiting from the widespread deployment of the wireless local area network (WLAN) infrastructure. However, with the expansion of application scenarios, this system suffers from labor-intensive work for received signal strength (RSS) fingerprint construction. In this article, we propose a crowdsourcing-based radio map construction method and trajectory matching algorithm to solve the time-consuming preliminary fingerprint data collection in the offline phase, where the tedious collection work is replaced by the massive crowdsourcing sensor data. First, the latent information of the map is extracted by using some image processing methods, and all possible routes are obtained simultaneously with the depth-first traversal method. Then, considering the strict restrictions on walking in indoor environments, the crowdsourcing trajectories are produced by matching the result of pedestrian dead reckoning (PDR) with the candidate routes based on the Shape Context algorithm. Further, a crowdsourcing trajectory can be represented by uniformly distributed reference points based on step detection, and each point corresponds to a unique RSS of access points (APs). Since such a fingerprint construction method can be performed while smartphone holders are moving and not aware of their actual positions, it can be referred to the dynamic construction method of the radio map. The real scenario experiments reveal that the proposed solution can significantly reduce the time and manpower consumption to build the radio map. Moreover, compared with traditional schemes, our crowdsourcing-based radio map construction method for the WiFi fingerprinting localization (CRCLoc) system can improve the accuracy and robustness of the indoor-positioning results.

A pedestrian POS for indoor Mobile Mapping System based on foot-mounted visual–inertial sensors

The mobile mapping system (MMS) is an enabling technology for indoor location-based services. The position and orientation system (POS) is one of the cores of the mobile mapping system. This paper proposes a pedestrian POS solution for infrastructure-free environments based on the fusion of foot-mounted IMU and stereo camera. The structure from motion (SFM) constructs the trajectory and environment under the condition with rich visual textures; the stereo camera detects loop-closure for mitigating the accumulated error; the foot-mounted pedestrian dead reckoning (Foot-PDR) provides reliable continuous trajectories using the IMU when the visual-based system degrades or crashes. The feasibility and reliability of the proposed system were verified by an office building test and an underground parking lot test. The proposed system achieved 0.237 m and 0.227 m position errors in these two scenarios, respectively. Moreover, the system can maintain the average position accuracy of 0.3 m when the camera shortly failed in some areas.

Smartphone-Based Unconstrained Step Detection Fusing a Variable Sliding Window and an Adaptive Threshold

Step detection for smartphones plays an important role in the pedestrian dead reckoning (PDR) for indoor positioning. Aiming at the problem of low step detection accuracy of smartphones in complex unconstrained states in PDR, smartphone-based unconstrained step detection method fusing a variable sliding window and an adaptive threshold is proposed. In this method, the dynamic updating algorithm of a peak threshold is developed, and the minimum peak value filtered after a sliding window filter is used as the adaptive peak threshold, which solves the problem that the peak threshold of different motion states is difficult to update adaptively. Then, a variable sliding window collaborative time threshold method is proposed, which solves the problem that the adjacent windows cannot be contacted, and the initial peak and the end peak are difficult to accurately identify. To evaluate the performance of the proposed unconstrained step detection algorithm, 50 experiments in constrained and unconstrained states are conducted by 25 volunteers holding 21 different types of smartphones. Experimental results show: The average step counting accuracy of the proposed unconstrained step detection algorithm is over 98%. Compared with the open source program Stepcount, the average step counting accuracy of the proposed algorithm is improved by 10.0%. The smartphone-based unconstrained step detection fusing a variable sliding window and an adaptive threshold has a strong ability to adapt to complex unconstrained states, and the average step counting accuracy rate is only 0.6% lower than that of constrained states. This algorithm has a wide audience and is friendly for different genders and smartphones with different prices.

An Enhanced Pedestrian Dead Reckoning Aided With DTMB Signals

Pedestrian Dead Reckoning (PDR) is a good alternative positioning method in Global Navigation Satellite Systems (GNSS)-challenged environments. It has the advantage in terms of infrastructure-independent, while the disadvantage is that, the estimated errors grow rapidly with time. The Digital Terrestrial Multimedia Broadcasting (DTMB) signals have strong transmission power, which contributes to better urban propagation and building penetration than GNSS signals. So, in this paper, an enhanced PDR algorithm aided with DTMB signals is proposed. In the study, a complete software-defined radio receiver is developed to estimate the Doppler speed and range information from DTMB signals. To reduce the accumulated errors of the PDR, the Extended Kalman Filter (EKF) algorithm is carried out to fuse the information of Doppler speed and ranging from the DTMB signals, as well as the information of walking speed and heading from the PDR. The results of the field tests showed that the 95% positioning error of the proposed algorithm is less than 3.94 m, while the PDR only is less than 9.19 m, which suggests the effectiveness of the proposed method to provide continuous positioning by fusing DTMB signals with PDR.

Pedestrian Inertial Navigation with Building Floor Plans for Indoor Environments via Particle Filter

&lt;p&gt;With the rapid development of smart city, the indoor positioning services became more and more important. During the existing solutions, inertial measurement unit (IMU) with pedestrian dead reckoning (PDR) was a promising scheme since they did not require external equipment in the environment. However, the orientation drift of low-cost IMU limited their application in practical. To address this problem, a zero-velocity update (ZUPT) framework included Kalman filter and particle filter is designed based on the foot-based low-cost IMU and digital floor plan to provide the service of personal navigation. In the designed Smoothing for ZUPT-aided INSs framework, the Kalman filter is used to estimate the position and attitude by zero velocity correction technique. Then, the particle filter is used to improve the localization and heading accuracy by map matching. The position estimation presented in this study achieves an average position error of 1.16 m. The experimental results show that the designed framework can solve the personal navigation problem in the case of building plan information assistance and help improve the accuracy and reliability of continuous position determination of personal navigation systems effectively. &lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;