Abstract Pedestrian dead reckoning (PDR) is an effective technology for pedestrian navigation. In PDR, the steps are detected with the measurements of self-contained sensors, such as accelerometers, and the position is updated with additional heading angles. A smartphone is usually equipped with a low-cost microelectromechanical system accelerometer, which can be utilized to implement PDR for pedestrian navigation. Since the PDR position errors diverge with the walking distance, the global navigation satellite system (GNSS) is usually integrated with PDR for more reliable position results. This paper implemented a smartphone PDR/GNSS via a Kalman filter and factor graph optimization (FGO). In the FGO, the PDR factor is modeled, and the states are correlated with a dead reckoning algorithm. The GNSS position is modeled as the “GNSS” factor to constrain the states at each step. With a graphic model representing the states and measurements, the state estimation is converted to a nonlinear least square problem, and we utilize the Georgia Tech Smoothing and Mapping graph optimization library to implement the optimization. We tested the proposed method on a Huawei Mate 40 Pro handset with a standard playground field test, and the field test results showed that the FGO effectively improved the smartphone position accuracy. We have released the source codes and hope that they will inspire other works on pedestrian navigation, i.e., constructing an adaptive multi-sensor integration system using FGO on a smartphone. 

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Implementation and performance analysis of the PDR/GNSS integration on a smartphone

In a smartphone, the global navigation satellite system (GNSS) receiver is dominant in providing position information for users. However, GNSS signals are vulnerable to the environment, i.e., city canyons and tunnels. Pedestrian dead reckoning (PDR) exploring human walking gaits is another effective method to determine pedestrian position. Accelerometers and gyroscope sensors are available in smartphones to detect pedestrians&#x2019; steps and carry out PDR. Aiming to improve smartphone position accuracy, we propose two different factor graph optimization (FGO)-PDR/GNSS integration models. First, the position from PDR and GNSS is integrated using FGO. Second, given the negative influences of the heading angle errors and the inconsistency between a smartphone and the pedestrian walking direction, PDR step length and GNSS are integrated using FGO. Android smartphones were utilized to collect different datasets and evaluate the proposed methods; experimental results indicated the superior performance of the proposed methods. 

Smartphone PDR/GNSS Integration via Factor Graph Optimization for Pedestrian Navigation

Positioning accuracy and power consumption are essential performance indicators of integrated navigation and positioning chips. This paper proposes a single-frequency GNSS/MEMS-IMU/odometer real-time high-precision integrated navigation algorithm with dynamic power adaptive adjustment capability in complex environments. It is implemented in a multi-sensor fusion navigation SiP (system in package) chip. The simplified INS algorithm and the simplified Kalman filter algorithm are adopted to reduce the computation load, and the strategy of adaptively adjusting the data rate and selecting the observation information for measurement update in different scenes and motion modes is combined to realize high-precision positioning and low power consumption in complex scenes. The performance of the algorithm is verified by real-time vehicle experiments in a variety of complex urban environments. The results show that the RMS statistical value of the overall positioning error in the entire road section is 0.312 m, and the overall average power consumption is 141 mW, which meets the requirements of real-time integrated navigation for high-precision positioning and low power consumption. It supports single-frequency GNSS/MEMS-IMU/odometer integrated navigation SiP chip in real-time, high-precision, low-power, and small-volume applications.

Dynamic Adaptive Low Power Adjustment Scheme for Single-Frequency GNSS/MEMS-IMU/Odometer Integrated Navigation in the Complex Urban Environment

Pedestrian dead reckoning (PDR) and global navigation satellite system are two popular solutions for pedestrian navigation with a smartphone. pedestrian dead reckoning (PDR) estimates the user’s position by analyzing their walking gaits, including step length and heading angle. However, PDR position errors can accumulate over time due to measurement noise. In contrast, GNSS generates position information by processing radio signals. However, these signals can be affected by blockage and interference. GNSS and PDR are often integrated using a Kalman filter (KF) to provide a more reliable solution. While current integration methods rely on position and velocity measurements, pseudo-range measurements for PDR and GNSS integration still need to be explored. To improve the accuracy of pedestrian position estimation in urban areas, we propose a walking-gaits-aided smartphone GNSS approach. This approach involves employing a factor graph optimization (FGO)-based GNSS/ PDR tight integration method. The FGO- GNSS/ PDR tight integration considers the pseudo-range measurements from each satellite, pedestrian position, and step length to optimize the position estimation. We introduce a fuzzy adaptive FGO (A-FGO) to enhance the accuracy further to suppress pseudo-range outliers. We conducted two experiments using a Samsung Galaxy A40 and Huawei Mate 40 Pro smartphones to evaluate the accuracy of the proposed methods. Our experimental results demonstrate that the proposed methods effectively improve the PDR/ GNSS position accuracy.

Walking Gaits Aided Mobile GNSS for Pedestrian Navigation in Urban Areas

Smartphone-based positioning has gained significant popularity due to its widespread availability. However, its reliance on GNSS as the primary positioning component poses limitations in challenging environments such as urban canyons, tunnels, and indoor areas, where GNSS signals are distorted or missed due to multi-path interference and non-line-of-sight (NLOS). To address this, sensor fusion techniques have been employed, with the integration of MEMS IMU being a common approach due to its complementary features with GNSS. For pedestrian localization, the fusion of PDR and GNSS is a classic architecture that commonly involves the use of the Kalman Filter (KF). Recently, researchers have started to consider the Factor Graph Optimization (FGO) algorithm for the fusion, due to its better accuracy. However, it is still necessary to asses its performance compared to the KF under different situations, such as various pedestrian movements. In this study, experimental evaluations during walking and running activities demonstrate that FGO consistently outperforms KF, resulting in a significant reduction of mean horizontal errors by 30.6% and 10% in walking and running, respectively. These findings highlight that between walking and running modes, the accuracy of FGO is influenced by the magnitude of PDR errors and the level of correlation in GNSS measurements, resulting in varying performance. Overall, FGO demonstrated superior efficacy compared to KF in enhancing the accuracy and reliability of smartphone-based positioning systems, especially in challenging pedestrian scenarios.

Comparison of FGO and KF for PDR-GNSS Fusion on Smartphone for Diverse Pedestrian Movements

According to well-described literature concerning the work history of multipath mitigation in the global navigation satellite systems (GNSS), multipath is still the most dominant factor in a challenging environment. There are unperturbed harsh circumstances where GNSS signals cannot reach and smartphone navigation is not possible. The main objective of this research is to find an accurate solution for pedestrian smartphone navigation in a multipath environment. Experiments are done with micro-electro-mechanical system (MEMS) sensors mounted on a smartphone, and no extra hardware is needed. The latest Android smartphone is used to log the data files of GNSS and MEMS sensors. This scheme has been classified in the synopsis, and a rectangular route with three perpendicular turns has been selected for a pedestrian walk. The data is preprocessed using a low pass filter to remove high-frequency noise and smooth the signal. The description of accumulative error produced by the heading and step size estimation has been reduced by implementing the indices of mean cumulative heading error and cumulative step length error, respectively. In the end, the suboptimal extended Kalman filter algorithm is used to fuse the data of GNSS and pedestrian dead reckoning (PDR) for final results. In this paper, we try to give a technique to provide accurate pedestrian smartphone navigation. The fusion results show that the prospective method explores the possibility to use smartphone navigation in any case when GNSS or PDR information is not available. Substantial simulations are implemented and corroborate that the schemed method is sturdier to use in a harsh environments. The aim is to achieve high-level accuracy with an ultra-low-cost solution.

Accurate and Direct GNSS/PDR Integration Using Extended Kalman Filter for Pedestrian Smartphone Navigation

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Accurate and Direct GNSS/PDR Integration Using Extended Kalman Filter for Pedestrian Smartphone Navigation