Pedestrian Dead Reckoning Algorithm Research Articles

Indoor positioning method for pedestrian dead reckoning based on multi-source sensors

To solve the problems of severe error accumulation and low accuracy of pedestrian trajectory estimation in traditional Pedestrian Dead Reckoning (PDR) positioning technology, this paper proposes a multi-sensor fusion indoor PDR algorithm. Firstly, a generalized likelihood ratio multi-threshold detection algorithm is employed to detect the gait of pedestrians. Then, a linear multi-source information fusion model is constructed for step length estimation. Next, the quaternion strap-down attitude solution is utilized and coupled with an improved particle filter-unscented Kalman filter algorithm to correct heading angle deviations. Finally, integrate them into the PDR algorithm to estimate the pedestrian's position. The proposed PDR method's relative positioning errors for indoor two-dimensional plane and three-dimensional space walking are 0.36 % and 0.435 %, respectively. Compared to four traditional positioning algorithms, it reduces errors by approximately 0.77 %∼1.18 % and 5.42 %∼11.69 %, respectively. Experimental results indicate that the proposed PDR method effective suppression of error accumulation, achieving more accurate indoor PDR results.

Construction of features database using different platform navigation solutions for aiding PDR algorithm

Nowadays, navigation can be accomplished based on several technologies. Some of these technologies rely solely on camera vision-based algorithms, whereas others rely solely on motion sensors’ data. Furthermore, some solutions combine vision-based algorithms with data from motion sensors. In this study, three platform navigation algorithms (stereo visual odometry technique, combining the distance calculated from the visual optical flow with estimated heading from gyroscope sensor measurements, and combining the distance calculated from the visual optical flow with estimated heading from magnetometer sensor measurements) were compared to select the most accurate among them to be employed for features database construction. This features database, which contains various well-described features with known three-dimensional world coordinates, can be utilized to help any indoor pedestrian dead reckoning (PDR) navigation solution as well as mapping purposes. Tests' results demonstrated that vision-based technology (stereo visual odometry technique) is more accurate than the others however it should be aided with additional constraints to improve navigation positional accuracy. Afterward, this chosen technology was used to construct a well-described features database which was checked to assess its accuracy.

An improved pedestrian dead reckoning algorithm based on smartphone built-in MEMS sensors

With the advantages of low cost, small size and lightweight, the smartphone has played a more and more important role in pedestrian indoor positioning. In particular, the application of Pedestrian Dead Reckoning (PDR) in the smartphone built-in MEMS sensors makes the application of smartphone more extensive. However, the zero-bias instability of the smartphone built-in MEMS sensors leads to the rapid accumulation of pedestrian trajectory calculation error. To solve this problem, we used the bias drift model and Kalman filter (KF) to denoise the original MEMS data. The dual-feature step detection model of peak domain and time domain was established to provide accurate step information for step length estimation and heading correction. Based on the Weinberg model, the three-steps constraint step length estimation (TCSLE) model was proposed to estimate step length accurately. Then, based on the improved heuristic drift elimination (iHDE), the adaptive drift elimination (ADE) model was proposed to identify different walking states. The correction models under different walking states were established to correct the heading angle accurately. Finally, the pedestrian trajectory was reconstructed using accurate step length and heading information. To verify the performance of the PDR algorithm based on the above model, three experimenters with different heights and genders were recruited, and three mobile phones with different sensor performance were selected. The experimenters moved smoothly and steadily with hand-held mobile phone, and 18 sets of experiments were carried out along two paths. The experiment results shown that the step length deviation was less than 1.4871 %, the horizontal positioning error was less than 1.6070 m, and the relative positioning error was less than 1.1816 %D. The proposed PDR algorithm has strong adaptability and robustness, and meets the needs of pedestrian indoor positioning.

Neural Networks-Based Wi-Fi/PDR Indoor Navigation Fusion Methods

Pedestrian dead-reckoning (PDR) technique has high accuracy in short-term positioning and is little affected by environments, but its cumulative error makes long-term positioning infeasible. Received signal strength (RSS) fingerprint positioning is immune to the cumulative error, but its poor radio signal propagation affected by indoor environments leads to a large positioning error. The extended Kalman filter (EKF) is widely applied to improve the two methods, but the approximation of nonlinearity still causes a large localization error. In this study, we propose a new approach based on neural networks for the Wi-Fi/PDR positioning fusion. The nonlinear mapping capability of back-propagation (BP) neural network is used to correct the EKF nonlinear approximation errors. Unlike the commonly used PDR algorithm, a series of pedestrian historical motion states are applied to train the long short-term memory (LSTM) and then make predictions. This method can significantly reduce cumulative errors in PDR. On-site experiments demonstrate that the proposed method achieves an average localization error of 1.18 m with 73% of the errors under 2 m, which outperforms EKF and unscented Kalman filter (UKF) by approximately 45% under the same test environment. Moreover, another fusion method using LSTM localization results as a priori information is described in this article. Different from the fusion at the resulting level of the previous method, this second method achieves the fusion at the algorithm level by limiting the Wi-Fi reference point (RP) search range through the LSTM localization results to reduce training and positioning time.

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.

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.

An Outdoor Pedestrian Localization Scheme Fusing PDR and VPR

With the popularity of phones, the demand for pedestrian location-based services has increased significantly. PDR (pedestrian dead reckoning algorithm) can provide pedestrians with real-time and continuous localization services in the outdoor environment. However, due to the accuracy of the built-in inertial sensor of the mobile phone collecting data, there is heading drift in PDR resulting in localization error. The localization method based on VPR (visual place recognition) can provide the accurate position of a single point. The VPR based on the lightweight model only generates a low amount of calculation. We propose an outdoor localization scheme fusing PDR and VPR to provide continuous localization service for outdoor pedestrian localization. The experiment results show that compared with the PDR algorithm, the ARMSE of the fusion localization scheme is reduced by 40% to 60%, and the final start-to-end error is reduced by 31.9 m. The fusion scheme can provide an accurate and continuous localization service.

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.

A REAL-TIME IMPROVED PEDESTRIAN DEAD RECKONING TRAJECTORY TRACKING ALGORITHM

Abstract. Accurate location of pedestrians plays a crucial role in emergency relief, traffic control, crowd behavior analysis and other aspects. Especially in the context of the COVID-19 pandemic in recent years, pedestrian location technology can help relevant departments to complete target screening more quickly.However, the Pedestrian Dead Reckoning algorithm can only calculate the target trajectory through the sensor return value, but can not carry out real-time trajectory correction and location.With the rapid development of deep learning, object detection and tracking technology based on computer vision has been applied to pedestrian location, but there are two challenges in the application process.Firstly, in the pedestrian gathering scene, the target number base is large, so the accuracy of the current detection algorithm needs to be improved and the model drift index of the tracking algorithm needs to be reduced. Secondly, there is a certain distortion between the real three-dimensional coordinate space of pedestrians and the two-dimensional image captured by the camera, and the transformation of the spatial coordinate of the target point is a technical difficulty.In this regard, first of all, to improve the accuracy of pedestrian target detection in crowded scenes, this paper adopts the method of improving the generalization of network to pedestrian target, and uses k-means algorithm to find the best prior frame of pedestrian, and sets the width to height ratio suitable for the target.Secondly, to solve the problem of model drift in the above tracking process, this paper proposes a binary classification model based on target appearance difference, which introduces target context information as a new target distinguishing feature when two or more targets are similar.Finally, in order to obtain more accurate coordinate position information, this paper combines the inverse perspective algorithm to calculate the target coordinates into the coordinates in the world coordinate system, and calculates the exact position of the target in the aerial view, as well as the distance between the targets or the current flow of people.In order to evaluate the effectiveness of the proposed algorithm, experiments on target detection, tracking and precise positioning were carried out in different intensity scenarios to verify the feasibility of the proposed method.

Fusing of a Continuous Output PDR Algorithm With an Ultrasonic Positioning System

The availability of inertial navigation sensors in smartphones has facilitated the development of pedestrian dead reckoning (PDR) models on a large scale. These models often consist of a step detection algorithm combined with a heading estimation routine. Common approaches to step detection include searching for peaks/valleys in the acceleration signal, principle frequency estimation, and machine learning techniques. Since the sensors embedded in smart devices are prone to noise, the position error grows unbounded if unchecked and requires periodical corrections based on external measurements. In this work, we propose a novel step detection algorithm based on sine-wave approximation of the acceleration signal. This method detects step fractions as well as full steps, which allows for continuous and real-time updates. The step detection algorithm is combined with a heading estimation routine described in our previous work to obtain a stand-alone PDR model. To mitigate error accumulation, we fuse the proposed model with position and heading measurements provided by a commercial indoor positioning system based on ultrasound. We evaluate the performance of the PDR and fused model in an open office environment, by walking along a trajectory while carrying a smartphone in hand or in the pocket. The results demonstrate the feasibility of the sine-wave approximation approach to step detection, as well as the expected benefits of fusing PDR with the ultrasonic system.

Learning-Based Complex Motion Patterns Recognition for Pedestrian Dead Reckoning

As the global navigation satellite system (GNSS) faces the problems of signals blocked by various objects and absorbed by many materials in indoor environments, many indoor positioning methods based on smartphones' inertial measurement unit (IMU) become increasingly applicable. Pedestrian dead reckoning (PDR) as one of the most universal methods, several algorithms with high accuracy have been proposed based on it. However, many algorithms are only applicable to scenes where pedestrians are in the “going forward” state. When the pedestrian's motions include complex patterns such as sidestepping and walking backward, PDR will not work normally as the estimated result is far from the real trajectory. In this paper, we design a deep learning model assisted PDR algorithm to recognize the pedestrian's complex motion patterns (going forward, walking backward, left sidestepping, right sidestepping) from the smartphones' IMU. We firstly employ one combined model of trained convolution neural network (CNN) and long short-term memory (LSTM) neural network to determine the correction angle for different motion patterns. Then, we estimate the pedestrian's motion direction by adding the correction angle to the attitude estimated by the inertial navigation system (INS) mechanization. Finally, the position can be computed by the estimated stride length and the corrected motion direction. Compared with different algorithms, the experimental results have proved that our algorithm can effectively process data in complex motion patterns.

Triggered INS/GNSS Data Fusion Algorithms for Enhanced Pedestrian Navigation System

The paper deals with Personal Navigation Systems based on sensors commonly found in small devices such as smartphones and wearables. This research field has recently received broad interest within the scientific community thanks to many novel applications, which span from health and entertainment up to human navigation, rescue operations in dangerous environments, goods delivering, and others. The proposed approach features an outdoor navigation system obtained via an event-triggered multi-rate size-varying Kalman filter, which exploits the complementary properties of both a ground-based positioning system and an on-board navigation system, namely a Global Navigation Satellite System and a Pedestrian Dead Reckoning algorithm based only on inertial measurements. The data fusion method is designed to exploit real-time estimates of heading and step length, provided by the PDR, with the position obtained from GNSS. The presented solution can be used to recognize how the time-varying features of the user gait evolve, and to remove systematic errors due to wrong calibration of the sensors or to environmental noises. The proposed implementation can also work at each step with reduced information from the sensors, thus covering both the case of missing data (e.g., poor satellite coverage), and the situation of different sampling rates for each source. This latter scenario can be enforced to strongly decrease the use of GNSS and so to improve the efficiency for future implementations in low-energy devices. Experimental results show both the accuracy of the basic algorithm and possible applications of the extended features.

Combination of Smartphone MEMS Sensors and Environmental Prior Information for Pedestrian Indoor Positioning.

In view of the inability of Global Navigation Satellite System (GNSS) to provide accurate indoor positioning services and the growing demand for location-based services, indoor positioning has become one of the most attractive research areas. Moreover, with the improvement of the smartphone hardware level, the rapid development of deep learning applications on mobile terminals has been promoted. Therefore, this paper borrows relevant ideas to transform indoor positioning problems into problems that can be solved by artificial intelligence algorithms. First, this article reviews the current mainstream pedestrian dead reckoning (PDR) optimization and improvement methods, and based on this, uses the micro-electromechanical systems (MEMS) sensor on a smartphone to achieve better step detection, stride length estimation, and heading estimation modules. In the real environment, an indoor continuous positioning system based on a smartphone is implemented. Then, in order to solve the problem that the PDR algorithm has accumulated errors for a long time, a calibration method is proposed without the need to deploy any additional equipment. An indoor turning point feature detection model based on deep neural network is designed, and the accuracy of turning point detection is 98%. Then, the particle filter algorithm is used to fuse the detected turning point and the PDR positioning result, thereby realizing lightweight cumulative error calibration. In two different experimental environments, the performance of the proposed algorithm and the commonly used localization algorithm are compared through a large number of experiments. In a small-scale indoor office environment, the average positioning accuracy of the algorithm is 0.14 m, and the error less than 1 m is 100%. In a large-scale conference hall environment, the average positioning accuracy of the algorithm is 1.29 m, and 65% of the positioning errors are less than 1.50 m which verifies the effectiveness of the proposed algorithm. The simple and lightweight indoor positioning design scheme proposed in this article is not only easy to popularize, but also provides new ideas for subsequent scientific research in the field of indoor positioning.

A Multi-Mode PDR Perception and Positioning System Assisted by Map Matching and Particle Filtering

Currently, pedestrian dead reckoning (PDR) is widely used in indoor positioning. Since there are restrictions on a device’s pose in the procedure of using a smartphone to perform the PDR algorithm, this study proposes a novel heading estimation solution by calculating the integral of acceleration along the direction of the user’s movement. First, a lightweight algorithm, that is, a finite state machine (FSM)-decision tree (DT), is used to monitor and recognize the device mode, and the characteristics of the gyroscope at the corners are used to improve the heading estimate performance during the linear phase. Moreover, to solve the problem of heading angle deviation accumulation on positioning, a map-aided particle filter (PF) and behavior perception techniques are introduced to constrain the heading and correct the trajectory through the wall after filtering. The results indicate that the recognition of phone pose can be 93.25%. The improved heading estimation method can achieve higher stability and accuracy than the traditional step-wise method. The localization error can reduce to approximately 2.2 m when the smartphone is held at certain orientations.

A Robust PDR/UWB Integrated Indoor Localization Approach for Pedestrians in Harsh Environments.

Wireless sensor networks (WSNs) and the Internet of Things (IoT) have been widely used in industrial, construction, and other fields. In recent years, demands for pedestrian localization have been increasing rapidly. In most cases, these applications work in harsh indoor environments, which have posed many challenges in achieving high-precision localization. Ultra-wide band (UWB)-based localization systems and pedestrian dead reckoning (PDR) algorithms are popular. However, both have their own advantages and disadvantages, and both exhibit a poor performance in harsh environments. UWB-based localization algorithms can be seriously interfered by non-line-of-sight (NLoS) propagation, and PDR algorithms display a cumulative error. For ensuring the accuracy of indoor localization in harsh environments, a hybrid localization approach is proposed in this paper. Firstly, UWB signals cannot penetrate obstacles in most cases, and traditional algorithms for improving the accuracy by NLoS identification and mitigation cannot work in this situation. Therefore, in this study, we focus on integrating a PDR and UWB-based localization algorithm according to the UWB communication status. Secondly, we propose an adaptive PDR algorithm. UWB technology can provide high-precision location results in line-of-sight (LoS) propagation. Based on these, we can train the parameters of the PDR algorithm for every pedestrian, to improve the accuracy. Finally, we implement this hybrid localization approach in a hardware platform and experiment with it in an environment similar to industry or construction. The experimental results show a better accuracy than traditional UWB and PDR approaches in harsh environments.

Combining a Modified Particle Filter Method and Indoor Magnetic Fingerprint Map to Assist Pedestrian Dead Reckoning for Indoor Positioning and Navigation.

Although advancement has been observed in global navigation satellite systems and these systems are widely used, they cannot provide effective navigation and positioning services in covered areas and areas that lack strong signals, such as indoor environments. Therefore, in recent years, indoor positioning technology has become the focus of research and development. The magnetic field of the Earth is quite stable in an open environment. Due to differences in building and internal structures, this type of three-dimensional vector magnetic field is widely available indoors for indoor positioning. A smartphone magnetometer was used in this study to collect magnetic field data for constructing indoor magnetic field maps. Moreover, an acceleration sensor and a gyroscope were used to identify the position of a mobile phone and detect the number of steps travelled by users with the phone. This study designed a procedure for measuring the step length of users. All obtained information was input into a pedestrian dead reckoning (PDR) algorithm for calculating the position of the device. The indoor positioning accuracy of the PDR algorithm was optimised using magnetic gradients of magnetic field maps with a modified particle filter algorithm. Experimental results reveal that the indoor positioning accuracy was between 0.6 and 0.8 m for a testing area that was 85 m long and 33 m wide. This study effectively improved the indoor positioning accuracy and efficiency by using the particle filter method in combination with the PDR algorithm with the magnetic fingerprint map.

Learning Individual Models to Estimate the Walking Direction of Mobile Phone Users

Pedestrian Dead Reckoning algorithms are commonly used to assist pedestrian navigation with handheld sensors. The estimation of the walking direction remains an important source of positioning error of mobile phone users since this direction may be different from the device’s pointing direction. A better understanding of human walking gait has enabled to produce new algorithms to mitigate the impact of the way the device is held in hand. WAISS algorithm is one of them. It is based on the study of horizontal hand accelerations and their modeling using Gaussian Mixture Models (GMM). However, ongoing search for universal modeling of handheld device carrying mode defeats the varying nature of human gait. This paper investigates the impact of individual gait characteristics and their modeling to improve the estimation of the walking direction. Different models are learned for curved and straight lines and varying GMM are proposed to account for inter-individual gait variations. This results in a reduced walking direction error with a 8.1° mean error to the 90th percentile computed for 3 subjects over a 1.5 km indoor/outdoor walk.

Research on Improved Pedestrian Dead Reckoning Algorithms

In view of user's requirements of high performance, easy operation and low cost with indoor location, data acquisition software based on Android platform utilize sensors built-in cellphone is used to study the pedestrian dead reckoning (PDR). Step detection and step length estimation are carried out by collecting leg swing angle while walking, and the classical step length estimation algorithm is improved. At the same time, the improved heuristic drift elimination algorithm is used to calculate the direction. Finally, the experiment results show that the PDR technology can better satisfy the requirements of indoor pedestrian positioning, and as for the low-cost multi-sensor. compared with the existing algorithm, the improved algorithm with higher positional accuracy.

MagPP: Combining Particle Filters and Pedestrian Dead Reckoning Algorithm with Geomagnetism for Indoor Positioning Using Smartphone

MagPP: Combining Particle Filters and Pedestrian Dead Reckoning Algorithm with Geomagnetism for Indoor Positioning Using Smartphone

Multimode Pedestrian Dead Reckoning Gait Detection Algorithm Based on Identification of Pedestrian Phone Carrying Position

Pedestrian dead reckoning (PDR) is an essential technology for positioning and navigation in complex indoor environments. In the process of PDR positioning and navigation using mobile phones, gait information acquired by inertial sensors under various carrying positions differs from noise contained in the heading information, resulting in excessive gait detection deviation and greatly reducing the positioning accuracy of PDR. Using data from mobile phone accelerometer and gyroscope signals, this paper examined various phone carrying positions and switching positions as the research objective and analysed the time domain characteristics of the three-axis accelerometer and gyroscope signals. A principal component analysis algorithm was used to reduce the dimension of the extracted multidimensional gait feature, and the extracted features were random forest modelled to distinguish the phone carrying positions. The results show that the step detection and distance estimation accuracy in the gait detection process greatly improved after recognition of the phone carrying position, which enhanced the robustness of the PDR algorithm.