Traditional Pedestrian Dead Reckoning 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.

A Hybrid CNN-LSTM-Based Approach for Pedestrian Dead Reckoning Using Multi-Sensor-Equipped Backpack

Researchers in academics and companies working on location-based services (LBS) are paying close attention to indoor localization based on pedestrian dead reckoning (PDR) because of its infrastructure-free localization method. PDR is the fundamental localization technique that utilize human motion to perform localization in a relative sense with respect to the initial position. The size, weight, and power consumption of micromechanical systems (MEMS) embedded into smartphones are remarkably low, making them appropriate for localization and positioning. Traditional pedestrian PDR methods predict position and orientation using stride length and continuous integration of acceleration in step and heading system (SHS)-based PDR and inertial navigation system (INS)-PDR, respectively. However, these two approaches provide accumulations of error and do not effectively leverage the inertial measurement unit (IMU) sequences. The PDR navigation solution relays on the standard of the MEMS, which yields PDR with the acceleration and angular velocity from the accelerometer and gyroscope, respectively. However, low-cost small MEMSs endure enormous error sources such as bias and noise. Hence, MEMS assessments lead to navigation solution drifts when utilized as inputs to the PDR. As a consequence, numerous methods have been proposed to mitigate and model the errors related to MEMS. Deep learning-based dead reckoning algorithms are provided to address aforementioned issues owing to the end-to-end learning framework. This paper proposes a hybrid convolutional neural network (CNN) and long short-term memory network (LSTM)-based inertial PDR system that extracts inertial measurement units (IMU) sequence features. The end-to-end learning framework is introduced to leverage the efficiency of low-cost MEMS because data-driven solutions provide more complete knowledge of the ever-increasing data volume and computational power over the filtering model approach. A CNN-LSTM model was employed to capture local spatial and temporal features. Experiments conducted on odometry datasets collected from multi-sensor backpack devices demonstrated that the proposed architecture outperformed previous traditional PDR methods, demonstrating that the root mean square error (RMSE) for the best user was 0.52 m. On the handheld smartphone-only dataset the best achieved R2 metric was 0.49.

Magnetic Field-Enhanced Learning-Based Inertial Odometry for Indoor Pedestrian

Pedestrian dead-reckoning (PDR) is a vital technique in pedestrian localization. Compared with traditional PDR, learning-based inertial odometry has the advantages of smaller position drift and is insensitive to pedestrian motion patterns. However, the heading drift of the trajectory is still the dominant error source for the position error drift in these methods. This study focuses on providing a pedestrian trajectory estimation method with low drift by properly fusing learned-based inertial odometry and magnetometer measurements under an indoor scenario containing significant magnetic field disturbance. The proposed method reduces the impact of magnetic field disturbance by adopting a long-term average magnetic vector, which is far more stable than using local magnetic vectors. Meanwhile, the proposed method can estimate the magnetometer bias online rather than depending on precalibrated magnetometer measurements. The test results show that the proposed method can obtain superior positioning performance using uncalibrated raw magnetometer data compared to other methods, even using calibrated magnetometer data. Simultaneously, this method achieves a balance between algorithm accuracy and efficiency.

An Adaptive Human Activity-Aided Hand-Held Smartphone-Based Pedestrian Dead Reckoning Positioning System

Pedestrian dead reckoning (PDR), enabled by smartphones’ embedded inertial sensors, is widely applied as a type of indoor positioning system (IPS). However, traditional PDR faces two challenges to improve its accuracy: lack of robustness for different PDR-related human activities and positioning error accumulation over elapsed time. To cope with these issues, we propose a novel adaptive human activity-aided PDR (HAA-PDR) IPS that consists of two main parts, human activity recognition (HAR) and PDR optimization. (1) For HAR, eight different locomotion-related activities are divided into two classes: steady-heading activities (ascending/descending stairs, stationary, normal walking, stationary stepping, and lateral walking) and non-steady-heading activities (door opening and turning). A hierarchical combination of a support vector machine (SVM) and decision tree (DT) is used to recognize steady-heading activities. An autoencoder-based deep neural network (DNN) and a heading range-based method to recognize door opening and turning, respectively. The overall HAR accuracy is over 98.44%. (2) For optimization methods, a process automatically sets the parameters of the PDR differently for different activities to enhance step counting and step length estimation. Furthermore, a method of trajectory optimization mitigates PDR error accumulation utilizing the non-steady-heading activities. We divided the trajectory into small segments and reconstructed it after targeted optimization of each segment. Our method does not use any a priori knowledge of the building layout, plan, or map. Finally, the mean positioning error of our HAA-PDR in a multilevel building is 1.79 m, which is a significant improvement in accuracy compared with a baseline state-of-the-art PDR system.

Hybrid Indoor Positioning System for Pedestrians With Swinging Arms Based on Smartphone IMU and RSSI of BLE

Over the last decades, researchers have invented many positioning algorithms for pedestrians in the indoor environment, such as trilateration, fingerprint, and pedestrian dead reckoning (PDR) system. However, indoor positioning is still difficult to solve with current technology. The fingerprint and trilateration based on received signal strength indicator (RSSI) usually require filters to stabilize the RSSI signal, which will cause a delay in positioning, which is fatal for real-time positioning. The PDR system with the inertial measurement unit (IMU) of smartphones usually requires the direction of the Y-axis of smartphones parallel with movement. Nevertheless, pedestrians will swing their arms while walking normally, which is not considered in the traditional PDR. In this case, a huge bias in heading estimation is inevitable, which causes the traditional PDR system to be unavailable during walking with swinging arms. In this article, a hybrid indoor positioning algorithm based on IMU and RSSI is proposed for pedestrians with swinging arms. The PDR system is improved by analyzing the characteristics of walking postures. In order to eliminate the cumulative error existing in the traditional PDR system, we propose the multipoint positioning algorithm based on RSSI for calibration. Combined with the Kalman filter, positioning delay and error have been effectively reduced for indoor location-based services (ILBSs).

The Technology of Crowd-Sourcing Landmarks-Assisted Smartphone in Indoor Localization

In recent years, with the popularity of smartphones, mobile sensor technologies have been widely used in indoor localization. Its advantage is that data can be collected directly from smartphones without installing any other equipment. Meanwhile, it has the disadvantages of high requirements on positioning conditions and large deviations in positioning results. A technology of crowd-sourcing landmarks-assisted smartphone in indoor localization (CLS-IL) is proposed in this paper so as to solve these disadvantages. Firstly, the accelerometer, magnetometer and gyroscope sensors are used to acquire initial positioning of users. The characteristics of magnetometer and gyroscope sensors are analyzed through a series of experiments in allusion to the key problem of heading estimation errors, then corresponding data is matched for different experimental conditions. After that, the corresponding landmark database is established for experimental environment and an adaptive optimization algorithm of landmarks is proposed to correct drift errors caused by the readings of sensors influenced by surrounding environment and long positioning time excessively during traditional pedestrian dead reckoning. Experimental results show that the location accuracy of CLS-IL system is significantly improved compared with traditional technologies, which provides strong support for subsequent studies.

Hybrid Navigation Method of INS/PDR Based on Action Recognition

In allusion to indoor passive low-cost shoe-mounted personal navigation system (PNS), inertial navigation system (INS), and pedestrian dead reckoning (PDR) each has strengths and weaknesses. In this paper, an INS/PDR hybrid navigation method (IPH) is introduced based on action recognition aiming at large position divergence caused by heading drift. The PDR algorithm for improved heading estimation (IHE) is proposed. It can provide pseudoheading measurement according to the result of action recognition, so it can restrain the heading angle drift effectively. Besides, the INS/PDR IPH is proposed. In the situation of normal walking, the proposed method introduces the corresponding PDR pseudoposition measurement information to the PNS filter for correction. The proposed method not only can reserve the comprehensive and independent navigation information of INS but also can incorporate PDR location information with high precision when walking normally. Thus, the combination of the advantages of INS and PDR is achieved without adding any sensors. The effectiveness of the method is verified by the motion experiments. The navigation results of the PDR algorithm based on IHE are improved by around 57% compared with the traditional PDR. In addition, the navigation results of the INS/PDR hybrid method are improved by 42% on average compared with the INS.

Axis-Exchanged Compensation and Gait Parameters Analysis for High Accuracy Indoor Pedestrian Dead Reckoning

Pedestrian dead reckoning (PDR) is an effective way for navigation coupled with GNSS (Global Navigation Satellite System) or weak GNSS signal environment like indoor scenario. However, indoor location with an accuracy of 1 to 2 meters determined by PDR based on MEMS-IMU is still very challenging. For one thing, heading estimation is an important problem in PDR because of the singularities. For another thing, walking distance estimation is also a critical problem for pedestrian walking with randomness. Based on the above two problems, this paper proposed axis-exchanged compensation and gait parameters analysis algorithm to improve the navigation accuracy. In detail, an axis-exchanged compensation factored quaternion algorithm is put forward first to overcome the singularities in heading estimation without increasing the amount of computation. Besides, real-time heading is updated by R-adaptive Kalman filter. Moreover, gait parameters analysis algorithm can be divided into two steps: cadence detection and step length estimation. Thus, a method of cadence classification and interval symmetry is proposed to detect the cadence accurately. Furthermore, a step length model adjusted by cadence is established for step length estimation. Compared to the traditional PDR navigation, experimental results showed that the error of navigation reduces 32.6%.

Electromyography-Based Locomotion Pattern Recognition and Personal Positioning Toward Improved Context-Awareness Applications

Personal positioning has been playing an important role in context awareness and navigation. Pedestrian dead reckoning (PDR) solution is a positioning technology used where the global positioning system (GPS) signal is not available or its signal is mightily attenuated or reflected by constructions nearby, such as inside the buildings or in GPS degraded areas such as urban city, basement. A traditional PDR solution employs a multisensor unit (integrating accelerometer, gyroscope, digital compass, barometer, etc.) to detect step occurrences, as well as to estimate the stride length. In our pilot research, we proposed a novel electromyography (EMG)-based method to fulfill that task and obtained satisfying PDR results. In this paper, a further attempt is made to investigate the feasibility of using EMG sensors in sensing muscle activities to detect the corresponding locomotion patterns, and as a result, a new approach, which recognizes different locomotion patterns using EMG signals and constructs stride length models according to the recognition results, is then proposed to improve the positioning accuracy and robustness of the EMG-based PDR solution by adapting the stride length model into different locomotion patterns. The experimental results demonstrate that EMG-based pattern recognition of four motions (walking, running, walking upstairs, walking downstairs) achieve an error rate of less than 2%. Combined with locomotion pattern recognition, the proposed EMG-based PDR solution yield a position deviation of less than 5 m within the whole distance of 404 m in a simulated indoor/outdoor field test. The proposed method is proven to be effective and practical in sensing context information, including both the user's activities and locations.

Comparison of EMG-based and Accelerometer-based Speed Estimation Methods in Pedestrian Dead Reckoning

In low-cost self-contained pedestrian navigation systems, traditional Pedestrian Dead Reckoning (PDR) solutions utilize accelerometers to derive the speed as well as the distance travelled, and obtain the walking heading from magnetic compasses or gyros. However, these measurements are sensitive to instrument errors and disturbances from ambient environment. To be totally different from these signals in nature, the electromyography (EMG) signal is a typical kind of biomedical signal that measures electrical potentials generated by muscle contractions from the human body. This kind of signal would reflect muscle activities during human locomotion, so that it can not only be used for speed estimation, but also disclose the azimuth information from the contractions of lumbar muscles when changing the direction of walking. Therefore, investigating how to utilize the EMG signal for PDR is interesting and promising. In this paper, a novel EMG-based speed estimation method is presented, including setup of the EMG equipment, pre-processing procedure, stride detection and stride length estimation. Furthermore, this method suggested is compared with the traditional one based on accelerometers by means of several field tests. The results demonstrate that the EMG-based method is effective and its performance in PDR can be comparable to that of the accelerometer-based method.