Reliable and continuous precise GNSS positioning serves as a cornerstone for intelligent transportation, robotic systems, and emerging internet of things (IoT) applications. However, achieving such reliability in urban environments remains challenging due to frequent carrier-phase outliers. Conventional anti-outlier methods are highly dependent on the accuracy of prior absolute pose from INS or other aiding sources. Once the pose estimates degrade, outlier detection may fail or misclassify GNSS measurements, leading to further degradation in positioning results, forming a vicious cycle. To address this problem, this paper proposes a novel carrier-phase outlier-resistance method that is immune to prior pose error. A two-degree-of-freedom TDCP/VDR model is designed to eliminate dependence on prior pose accuracy by leveraging high-precision VDR relative poses and conducting a detailed error analysis of the TDCP and VDR models. Based on this model, we construct a two-step framework that integrates an improved RANSAC approach with a multi-epoch finite state machine (FSM) for reliable outlier detection. Experimental results in complex urban scenarios demonstrate that the proposed method achieves a horizontal position error (CEP95) of approximately 1 m, improving by 63.6% and 56.7% over the IGG-III and Tukey robust methods, respectively, while maintaining the maximum horizontal position error below 2 m. Ablation experiments show that the proposed method maintains strong outlier resistance even with prior absolute position disturbances up to 300 m. Cycle-slip simulations demonstrate high detection recall even when up to 75% of satellites are contaminated by outliers.

The pose-only Kalman filter (PO-KF) for visual-inertial odometry (VIO) has demonstrated comparable localization accuracy to optimization-based systems while retaining the computational efficiency of filter-based approaches. However, the performance of point-based PO-KF degrades in textureless environments due to the scarcity of point features, while line features can serve as valuable supplements. To exploit the benefits of pose-only line-feature representation, we extend PO-KF with a pose-only line-feature measurement model and propose PLPO-KF, a unified pose-only representation-based Kalman filter for point-line-based VIO. Specifically, to eliminate reliance on 3D line reconstruction and enable immediate line updates, we develop a trifocal tensor-based pose-only line-feature measurement model with a concise and clear measurement equation. In addition, we introduce an INS-enhanced line optical-flow tracking method to ensure robust and accurate line feature association across multiple frames. Extensive experimental results on both public and private datasets demonstrate that PLPO-KF consistently outperforms state-of-the-art point-based and point-line-based VIO systems. Ablation studies further exhibit the effectiveness of both the proposed line pose-only measurement model and the INS-enhanced line tracking method.

High-precision positioning plays a pivotal role in intelligent transportation systems by enabling reliable navigation, automated intersection management and high-precision platooning for autonomous vehicles in large-scale, unfamiliar environments. However, existing loosely or tightly coupled integration solutions of Global Navigation Satellite System (GNSS), Inertial Navigation System (INS), and Visual System can be challenged by the limited availability of GNSS signals, which will affect their ability to consistently achieve robust high-precision positioning in harsh environments. In this work, we propose a multi-sensor (GNSS/INS/Vision) ultra-tightly coupled system with GNSS carrier phase long coherent integration tracking (MUT-LCI). At the signal processing level, MUT-LCI utilizes the multi-sensor sensed dynamics to assist GNSS signal tracking loops and realizes extended coherent integration periods up to 300 milliseconds, improving the availability of GNSS carrier phase observations in highly challenging environments significantly. The data fusion of the GNSS RTK/INS/Vision is conducted using a Multi-state Constraint Kalman Filter (MSCKF). To evaluate the performance of the proposed MUT-LCI system, we conducted experiments using an intelligent transportation wheeled robot platform in harsh environments, including areas covered by dense tree canopies and surrounded by tall buildings. The results demonstrate that the proposed MUT-LCI system achieves precise GNSS carrier phase signal tracking in highly challenging environments, enabling continuous RTK fixed solutions and providing reliable centimeter-level positioning accuracy.

Robust and accurate proprioceptive state estimation of the main body is crucial for legged robots to execute tasks in extreme environments where exteroceptive sensors, such as LiDARs and cameras, may become unreliable. In this paper, we propose DogLegs, a state estimation system for legged robots that fuses the measurements from a body-mounted inertial measurement unit (Body-IMU), joint encoders, and multiple leg-mounted IMUs (Leg-IMU) using an extended Kalman filter (EKF). The filter system contains the error states of all IMU frames. The Leg-IMUs are used to detect foot contact, thereby providing zero-velocity measurements to update the state of the Leg-IMU frames. Additionally, we compute the relative position constraints between the Body-IMU and Leg-IMUs by the leg kinematics and use them to update the main body state and reduce the error drift of the individual IMU frames. Field experimental results have shown that our proposed DogLegs system achieves better state estimation accuracy compared to the traditional leg odometry method (using only Body-IMU and joint encoders) across various terrains. We make our datasets publicly available to benefit the research community (https://github.com/YibinWu/leg-odometry).

The foot-mounted inertial navigation system (Foot-INS) is a crucial technology for professional pedestrian positioning, unaffected by environmental conditions. However, due to the unobservable nature of the absolute heading, single or dual Foot-INS configurations suffer from significant position drift errors. This paper introduces an innovative pedestrian dead reckoning (PDR) method that combines foot-mounted and waist-mounted IMU with magnetic field vector constraints. Leveraging the fact that the displacements of the foot and waist are consistent when the foot makes ground contact, the proposed method uses the relative displacement estimated by Foot-INS to correct the waist-mounted INS. Building on this, a relative magnetic field vector constraint method is developed using error state clonal Kalman filtering, capitalizing on the similarity of magnetic interference within a local area. The results from 12 tests conducted in typical indoor environments, such as offices and underground parking lots, demonstrate that the proposed method significantly enhances positioning performance in areas with frequent magnetic interference. The positioning error is reduced by more than 49% compared to single or dual Foot-INSs.

Pedestrian navigation using smart devices has become increasingly prevalent in daily life. Typically, the user’s location in outdoor environments can be obtained via the embedded global navigation satellite system (GNSS) chip. However, in urban environments, the performance of conventional GNSS positioning is significantly degraded due to multipath effects and non-line-of-sight (NLOS) receptions that ruin the GNSS observations. To address this challenge, this paper proposes a robust GNSS/inertial navigation system (INS) integrated system for pedestrian navigation in urban environments. Based on robust optimization algorithm, the proposed method utilizes the spatial consistency between multiepoch GNSS pseudorange observations and pedestrian dead reckoning (PDR) trajectory to detect GNSS outliers. Then, the fault-free GNSS observations are integrated with INS-based PDR via the conventional robust Kalman filter (RKF) in tightlycoupled mode. To validate the performance of the proposed method, nine sets of test data were collected covering three typical urban scenarios. Experimental results show that the proposed method achieves an average horizontal positioning accuracy of 10.21 m (95%), compared to 17.27 m for the conventional RKF, representing an improvement of approximately 41

Visual-inertial state estimation is widely employed in the Internet of Things, with filter-based visual-inertial odometry (VIO) being a popular algorithm due to its balance between computational efficiency and localization accuracy. However, the localization performance of the commonly used multi-state constraint Kalman filter (MSCKF)-based VIO is suffering from linearization errors in feature three-dimensional (3D) positions and delayed measurement updates. Targeting more accurate and robust localization, we incorporate the pose-only representation into the filter-based VIO and propose a pose-only representation-based Kalman filter (PO-KF) in this paper. Leveraging the decoupling of camera poses and feature positions in the pose-only representation, the proposed PO-KF explicitly eliminates feature 3D coordinates from its measurement equation. As a result, the linearization errors caused by feature positions can be removed efficiently, while immediate updates of visual measurements can be conducted. We also introduce an information matrix-derived base-frame selection algorithm to identify the most suitable base-frames for each feature. Extensive experiments on multiple datasets demonstrate that PO-KF outperforms state-of-the-art VIO systems. Notably, PO-KF achieves nearly a 50% reduction in relative pose errors compared to MSCKF-based VIO. Further experiments demonstrate that PO-KF also exhibits superior robustness while maintaining real-time performance comparable to MSCKF-based VIO.

Accurate and rapid INS state initialization is crucial to ensure the performance of vehicular GNSS/INS integrated navigation. However, in typical urban environments (such as under viaducts and urban canyons), existing GNSS-assisted INS state initialization methods are sensitive to observation outliers. This paper proposes a robust INS state initialization method for vehicle-mounted GNSS/INS integrated navigation. The proposed method first derives the error propagation between the short-term relative navigation (i.e., position, velocity, attitude) and the INS initial state and the GNSS observation error model; then, the high-precision relative pose generated by INS is used to construct constraints between GNSS observation sequences, and the INS state initialization problem is converted into an optimization problem; finally, a two-step optimization strategy is designed to improve the problem of high computational complexity in solving the full-state optimization problem. We use six datasets collected in a typical urban environment to verify the feasibility of the proposed method. The proposed method uses observation sequences within a 10-second to initialize the heading, velocity, and horizontal position with errors of 2.50∘, 0.30 m/s, and 11.1 m, respectively, which are reduced by 73%, 41%, and 14% compared with existing methods.

Location-based services (LBS) have become an integral part of daily life and work for the general public. However, achieving widespread and accurate positioning in typical indoor environments remains a significant challenge, particularly in multi-floor indoor parking lots where radio frequency signals like WiFi are often unavailable. Indoor magnetic matching presents a viable solution, but it requires reducing mapping costs through the use of crowdsourced data. To tackle this issue, we propose an innovative method for constructing magnetic maps using crowdsourced vehicle data. Our approach introduces a multi-user joint vehicle dead reckoning technique based on graph optimization, which provides consistent directional estimates of crowdsourced vehicle trajectories. Subsequently, we establish associations between different vehicle trajectories using multi-attribute features of the magnetic field. Building on this foundation, we propose a global trajectory optimization with inequality and equality constraints to achieve precise estimation of crowdsourced vehicle trajectories. Testing with simulated data from two three-floor underground parking lots demonstrates that the proposed method, utilizing only on-board smartphone sensor data, achieves plane and elevation errors of less than 2.75 meters (95%) and 0.59 meters (95%), respectively. Additionally, the magnetic matching positioning error based on crowdsourced magnetic sequence maps is less than 2.29 meters (95%).

Bundle adjustment (BA) has been proven to improve the accuracy of the LiDAR mapping, but has not yet been properly employed in a dead-reckoning navigation system. In this paper, we present a frame-to-frame (F2F) BA for LiDAR-inertial navigation, named BA-LINS. Based on the direct F2F point-cloud association method, the same-plane points are associated among the LiDAR keyframes. Hence, the F2F plane-point BA measurement model can be constructed using the same-plane points. The LiDAR BA and the inertial measurement unit (IMU)-preintegration measurements are tightly coupled under the framework of factor graph optimization. Meanwhile, an effective adaptive covariance estimation algorithm for LiDAR BA measurements is proposed to improve the accuracy further. Exhaustive experiment results on public and private datasets demonstrate that BA-LINS yields superior accuracy to state-of-the-art methods. Compared to the baseline system FF-LINS, the absolute translation accuracy and state-estimation efficiency of BA-LINS are improved by 29.5% and 28.7%, respectively. Besides, the proposed adaptive covariance estimation algorithm exhibits notably improved accuracy and robustness.

A long-term accurate and robust localization system is essential for mobile robots to operate efficiently outdoors. Recent studies have shown the significant advantages of the wheel-mounted inertial measurement unit (Wheel-IMU)-based dead reckoning system. However, it still drifts over extended periods because of the absence of external correction signals. To achieve the goal of long-term accurate localization, we propose Wheel-GINS, a Global Navigation Satellite System (GNSS)/inertial navigation system (INS) integrated navigation system using a Wheel-IMU. Wheel-GINS fuses the GNSS position measurement with the Wheel-IMU via an extended Kalman filter to limit the long-term error drift and provide continuous state estimation when the GNSS signal is blocked. Considering the specificities of the GNSS/Wheel-IMU integration, we conduct detailed modeling and online estimation of the Wheel-IMU installation parameters, including the Wheel-IMU leverarm and mounting angle and the wheel radius error. Experimental results have shown that Wheel-GINS outperforms the traditional GNSS/Odometer/INS integrated navigation system during GNSS outages. At the same time, Wheel-GINS can effectively estimate the Wheel-IMU installation parameters online and, consequently, improve the localization accuracy and practicality of the system. The source code of our implementation is publicly available (https://github.com/i2Nav-WHU/Wheel-GINS).

Indoor positioning is an important part of supporting the Internet of Things and location-based services. Crowdsourcing-based magnetic map construction is a key technology to realize wide-area consumer indoor positioning. However, current crowdsourcing-based magnetic map schemes are not suitable for typical indoor scenarios (e.g., shopping malls). The reason is that they ignore the characteristics of crowdsourced data, including short-term trajectory, various pedestrian motion patterns, large-scale data set, and so on. In this article, we propose a novel crowdsourcing-based magnetic map construction method. First, learning-based inertial odometry is used to recover precise user motion trajectories regardless of changes in motion patterns. Then, a keyframe-efficient association method of magnetic time–frequency features is proposed, which is suitable for short-term trajectories of various shapes. Finally, a two-step global estimation optimization is proposed to further eliminate false associations of keyframes and improve the robustness of the method. The feasibility of the proposed method is verified by using a multiuser data set in a typical shopping mall scenario. The proposed method takes a total of 60.8 s to process a 12-h data set (subtrajectories with a duration of 90 s), and the average position error is 1.48 m (with scale correction) and 2.53 m (without scale correction). Compared with the existing crowdsourcing-based magnetic map scheme, the proposed method has been significantly improved in terms of feasibility, accuracy, and efficiency.

Lane-level positioning is a critical technology for supporting assisted driving and autonomous driving applica-tions. However, the Global Navigation Satellite System often falls short in providing reliable positioning (GNSS) due to signal attenuation, obstructions, and multipath in urban areas. Fortunately, typical challenging urban environments, such as tunnels and viaducts, create rich magnetic field features due to abundant ferromagnetic structures, offering an opportunity for magnetic field matching methods to achieve high-precision positioning. This paper presents a novel magnetic field matching/GNSS/Inertial Navigation System (INS) fusion algorithm designed for continuous lane-level positioning in complex environments using cost-effective sensors and computation-saving algorithm. Based on the traditional GNSS/INS tight integration algorithm, this research ensures the performance of the positioning system by enhancing the magnetic field matching and fusion positioning algorithms. First, a coarse-fine magnetic profile matching method is proposed to address the accuracy degradation resulting from the travel distance error of INS-derived trajectory. Second, the magnetic field matching position updates are performed in the vehicle frame, which enables more precise position error modeling. The proposed solution is evaluated through five field tests, covering over 200 kilometers of challenging urban roads. The results demonstrate mean CDF95 position errors of 2.09 m, 1.09 m, and 0.87 m in the forward, lateral, and vertical directions, respectively, and 94.67% accuracy on lane-determination.

Continuous and highly accurate positioning of land vehicles continues to be a substantial challenge in urban GNSS-denied environments. Although the vehicle motion model (VMM) is fused to mitigate the positioning error, the problems of position error accumulation over a distance remain. Hence, we introduce an innovative multi-information integrated navigation approach that leverages visual semantics in conjunction with a lightweight high-definition (LHD) map for absolute position refinement. This method enhances the navigation solution by integrating a vehicle-mounted GNSS/INS system with the precise localization capabilities of road semantics, such as lane lines and poles, through camera vision. We establish a comprehensive road semantic measurement model in the pixel frame to directly use raw pixel data for a tightly coupled integration process. Additionally, we examine the distinct contributions of lane lines and poles to the estimation of navigation error states using a simplified measurement model. Field tests with land vehicles demonstrate the efficacy of our proposed method and show that the longitudinal and lateral positioning errors decrease to 0.43 meters and approximately 0.27 meters, which are significant enhancements due to road semantic cues.

The carrier phase measurements of the global navigation satellite system (GNSS) exhibit degraded accuracy and availability in challenging environments, thereby hindering their effective use for intelligent transportation applications. In this paper, an inertial navigation system (INS) aided GNSS signal tracking architecture with long coherent integration periods is proposed to provide accurate and continuous carrier phase observations in challenging environments. In order to implement efficient long coherent integration, a GNSS real time kinematic (RTK)/INS ultra-tight integration algorithm is designed to compensate for the dynamics induced by the receiver-satellite relative motion, and a multichannel cooperative loop with a specialized discriminator is proposed to eliminate the negative effects due to the receiver oscillator instability. Meanwhile, an accurate open loop tracking strategy is designed to sustain the integer ambiguity during a signal blockage, improving the continuity of the carrier phases. Initial blockage test shows that the open loop strategy can keep the carrier phase error below 90 degrees for over 40 seconds. Moreover, an INS based cumulative cycle slip decision variable (CCSDV) is proposed to detect the carrier phase cycle slips accurately. Finally, field vehicle tests were carried out using a (micro-electro-mechanical system) MEMS IMU. It turns out the proposed architecture significantly improves the accuracy and continuity of the carrier phase observations and it can provide superior positioning performance with a small amount of GNSS single frequency observations and a long RTK baseline.

Foot-mounted inertial navigation system (Foot-INS) with the zero velocity update (ZUPT) has become one of the indispensable technical means in professional pedestrian positioning fields due to the advantages of self-constraint and immune to environmental factors. The dual-Foot-INS can provide more excellent autonomous positioning performance than a single-Foot-INS because it utilizes more opportunities for zero velocity correction and additional distance constraint information. However, the classical dual-Foot-INS does not fully exploit the distance constraint potential for positioning improvement. In this article, we proposed a novel minimum distance constraint (MDC) method that achieves higher positioning accuracy than the traditional dual-Foot-INS methods. To obtain an accurate and consistent state estimation under the nonlinear distance constraint problem, we propose an iterative distance constraint (IDC) algorithm. The IDC is transformed into an approximate linear constraint model, and an alternative estimate is obtained by the estimation projection method. To solve the problem that the distance constraint moment in the traditional method is affected by the recursive foot positions, we propose a more reasonable and reliable minimum distance moment detection (MDMD) method. The proposed MDMD method maximizes the positioning performance improvement of the dual-foot pedestrian system. Two rigorous experimental tests with a long walking trajectory without turn around and closed loop were conducted to verify the effectiveness of the proposed method, the positioning error of the proposed method is reduced by 83.5% and 62.9% compared to the classical ZUPT and MDC methods, respectively.

The 3-D position estimation of pedestrians is a vital module to build the connections between persons and things. The traditional gait model-based methods cannot fulfill the various motion patterns. And the various data-driven-based inertial odometry solutions focus on the 2-D trajectory estimation on the ground plane, which is not suitable for augmented reality (AR) applications. Tight learned inertial odometry (TLIO) proposed an inertial-based 3-D motion estimator that achieves very low position drift by using the raw inertial measurement unit (IMU) measurements and the displacement prediction coming from a neural network to provide low drift pedestrian dead reckoning. However, TLIO is unsuitable for mobile devices because it is computationally expensive. In this article, a lightweight learned inertial odometry network (LLIO-Net) is designed for mobile devices. By replacing the network in TLIO with the LLIO-Net, the proposed system shows a similar level of accuracy but remarkable efficiency improvement. Specifically, the proposed LLIO algorithm was implemented on mobile devices and compared the computational efficiency with TLIO. The inference efficiency of the proposed system is up to 12 times improved than that of TLIO. Source code can be found onhttps://github.com/i2Nav-WHU/LightweightLearnedInertialOdometergithub.

A reliable self-contained navigation system is essential for autonomous vehicles. Based on our previous study on Wheel-INS[1], a wheel-mounted inertial measurement unit (Wheel-IMU)-based dead reckoning (DR) system, in this paper, we propose a multiple IMUs-based DR solution for the wheeled robots. The IMUs are mounted at different places on the wheeled vehicles to acquire various dynamic information. In particular, at least one IMU has to be mounted at the wheel to measure the wheel velocity and take advantage of the rotation modulation. The system is implemented through a distributed extended Kalman filter structure where each subsystem (corresponding to each IMU) retains and updates its own states separately. The relative position constraints between the multiple IMUs are exploited to further limit the error drift and improve the system’s robustness. Particularly, we present the DR systems using dual Wheel-IMUs, one Wheel-IMU plus one vehicle body-mounted IMU (Body-IMU), and dual Wheel-IMUs plus one Body-IMU as examples for analysis and comparison. Field tests illustrate that the proposed multi-IMU DR system outperforms the single Wheel-INS in terms of both positioning and heading accuracy. By comparing with the centralized filter, the proposed distributed filter shows unimportant accuracy degradation while holding significant computation efficiency. Moreover, among the three multi-IMU configurations, the one Body-IMU plus one Wheel-IMU design obtains the minimum drift rate. The position drift rates of the three configurations are 0.82% (dual Wheel-IMUs), 0.69% (one Body-IMU plus one Wheel-IMU), and 0.73% (dual Wheel-IMUs plus one Body-IMU), respectively.

Acquiring accurate and reliable pedestrian trajectories is essential for providing indoor location-based services. Although a foot-mounted inertial navigation system (Foot-INS) can acquire pedestrian trajectories in multistory buildings, it will inevitably encounter heading divergence because the constraint information is not always valid. Therefore, we proposed an accurate and convenient postprocessing indoor pedestrian positioning system (IPPS) to acquire pedestrian trajectories in multistory buildings such as shopping malls. Based on the hypotheses that the start and end points of the pedestrian trajectories on a single floor were closed, and the horizontal position of the closing point on each floor was identical. Therefore, in the single floor of multistory buildings, we use the closing point to control the trajectory drift error caused by the Foot-INS, and use a smoothing algorithm to reasonably distribute the drift error to the entire trajectory. Heading divergence is unavoidable with the Foot-INS, result in the pedestrian trajectories acquired on different floors were rotationally offset. Because pedestrian trajectories can epitomize the building orientation and provide an opportunity to align those trajectories on multistory buildings, an algorithm was proposed to match the trajectories acquired on different floors. A hybrid simulation experiment was conducted using an accurate reference object to evaluate the positioning performance of the proposed IPPS. The effectiveness of acquiring pedestrian trajectories was also confirmed by various experimental tests in a large shopping mall. The study findings suggest that the proposed IPPS is self-contained, low cost, and has the potential for large-scale applications.

Railway track is never perfect, as rail distortions, namely, geometric irregularities, exist at all locations along the track. However, these distortions can be regarded as valuable indicators for train localization, since track irregularities present location-dependent characteristics, the measurements of which using onboard sensors are repeatable for the same track. In this research, we study the possibility of determining a train’s position by matching the track irregularity measurements to a predefined map. A train-borne experiment on a real track is used to preliminarily demonstrate the feasibility, evaluate the performance and determine the key parameters for practical implementation. The results show that a submeter longitudinal localization accuracy can be achieved even when using a low-cost cabin-mounted microelectromechanical system (MEMS) inertial measurement unit (IMU), which measures the train’s responses to track irregularities. The proposed method can enhance the positioning accuracy and improve the robustness of multisensory train localization systems.

The inertial navigation system (INS) has been widely used to provide self-contained and continuous motion estimation in intelligent transportation systems. Recently, the emergence of chip-level inertial sensors has expanded the relevant applications from positioning, navigation, and mobile mapping to location-based services, unmanned systems, and transportation big data. Meanwhile, benefit from the emergence of big data and the improvement of algorithms and computing power, machine learning (ML) has become a consensus tool that has been successfully applied in various fields. This article reviews the research on using ML technology to enhance inertial sensing from various aspects, including sensor design and selection, calibration and error modeling, navigation and motion-sensing algorithms, multi-sensor information fusion, system evaluation, and practical application. It summarizes the state of the art, advantages, and challenges on each aspect, and points out future research directions.

Localization techniques are becoming key to add location context to the Internet-of-Things (IoT) data without human perception and intervention. Meanwhile, the newly emerged low-power wide-area network (LPWAN) and 5G technologies have become strong candidates for mass-market localization applications. However, various error sources have limited localization performance by using such IoT signals. This article reviews the IoT localization system through the following sequence: IoT localization system review, localization data sources, localization algorithms, localization error sources and mitigation, and localization performance evaluation. Compared to the related surveys, this article has a more comprehensive and state-of-the-art review on IoT localization methods, an original review on IoT localization error sources and mitigation, an original review on IoT localization performance evaluation, and a more comprehensive review of IoT localization applications, opportunities, and challenges. Thus, this survey provides comprehensive guidance for peers who are interested in enabling localization ability in the existing IoT systems, using IoT systems for localization, or integrating IoT signals with the existing localization sensors.

Nonholonomic constraint (NHC) and odometer speed have been proven to significantly improve the navigation accuracy of a global navigation satellite system (GNSS)-aided inertial navigation system (INS) for land vehicular applications. Exploiting the full potential of the NHC and odometer aids requires the inertial measurement unit (IMU) mounting angles, i.e., angular misalignment with respect to the host vehicle, to be precisely known. We address the accurate estimation of the IMU mounting angles through an aided dead reckoning (DR) approach. In this method, DR using the GNSS/INS integrated attitude and distance traveled is fused with the GNSS/INS integrated position through a straightforward Kalman filter. Simulation and field tests are carried out to validate the proposed algorithm for different grade IMUs, including typical navigation-grade, tactical-grade and low-cost IMUs. The results demonstrate that the pitch and heading mounting angles can be estimated with a comparable accuracy with the GNSS/INS attitude solution, for example, 0.001° accuracy can be achieved for a navigation-grade GNSS/INS integrated system. The roll mounting angle can not be estimated due to lack of observability in this approach, and the heading mounting angle estimation may be influenced by the GNSS/INS heading accuracy drift to some extent for the low-cost IMUs.

Aimed at the challenge of generating indoor localization databases with daily life crowdsourcing-based inertial sensor data, this paper proposes an anchor point-based forward–backward smoothing method to obtain reliable localization solutions. More importantly, a quantitative framework is proposed to evaluate the quality of smartphone-based inertial sensor data automatically without user intervention. Through this framework, the reliability of each inertial sensor data can be evaluated and sorted. Tests with multiple people and multiple smartphones in a public office building and a shopping mall illustrate that the proposed method can provide a WiFi fingerprinting database that has similar accuracy to that generated by a supervised map-aided database-generation method. Therefore, the proposed method and framework can guide the promotion of crowdsourcing-based Internet of Things applications in the context of big data.

This paper provides a real-time attitude determination algorithm using gyros, accelerometers, and magnetometers on consumer portable devices. The main advantage of this algorithm is that uses a Kalman filter algorithm and utilizes multi-level constraints, including pseudo-observation updates, measurements from accelerometers and magnetometers, and the quasi-static attitude updates. Walking tests with different brands of smartphones showed that the algorithm provided promising absolute heading results outdoors, and provided smooth relative heading results indoors with different phone places such as handheld, at an ear, dangling with hand, and in a pants pocket.

This paper outlines an indoor navigation algorithm that uses multiple kinds of sensors and technologies, such as 9-axis sensors (i.e., 3D gyros, accelerometers, and magnetometers), WiFi, and magnetic matching. The corresponding real-time software on smartphones includes modules such attitude determination and gyro bias estimation, pedestrian dead-reckoning (PDR), WiFi positioning, and magnetic matching. The heading from the attitude-determination module is fed into the PDR-based position-tracking module. Then, PDR is used for providing continuous position solutions and for the blunder detection of both WiFi fingerprinting and magnetic matching. Meanwhile, WiFi fingerprinting utilizes a point-by-point matching technology, while magnetic matching is based on profile-matching. Finally, WiFi and magnetic matching results are passed into the position-tracking module as updates. This algorithm was tested with two smartphones in two indoor environments. The results indicates the proposed navigation algorithm provided better navigation results than those of PDR, WiFi, or magnetic matching by itself, and better than the results of PDR/WiFi and PDR/magnetic matching (MM) in challenging indoor environment. The proposed using off-the-shelf sensors available in consumer portable devices and existing WiFi infrastructures, and have been realized on smartphones.

This paper presents an indoor navigation algorithm that uses multiple kinds of sensors and technologies, such as MEMS sensors (i.e., gyros, accelerometers, magnetometers, and a barometer), WiFi, and magnetic matching. The corresponding real-time software on smartphones includes modules such dead-reckoning, WiFi positioning, and magnetic matching. DR is used for providing continuous position solutions and for the blunder detection of both WiFi fingerprinting and magnetic matching. Finally, WiFi and magnetic matching results are passed into the position-tracking module as updates. Meanwhile, a barometer is used to detect floor changes, so as to switch floors and the WiFi and magnetic databases. This algorithm was tested during the 5th EvAAL indoor navigation competition. Position errors on three quarters (75 %) of test points (totally 62 test points were selected to evaluate the algorithm) were under 6.6 m.

A novel calibration architecture that calculates the gyro biases without any external equipment and without any user intervention is proposed. This architecture uses a Kalman filter algorithm and utilizes multi-level constraints, such as pseudo-observation updates and the accelerometer and magnetometer measurements. Walking tests with smartphones show that the proposed architecture is effective and accurate when being used under various scenarios with different phone contexts.

For vehicle navigation, Global Positioning System (GPS) provides long term accurate measurements, but only when a direct line of sight to four or more satellites exists. Inertial navigation systems (INS), on the other hand, are self contained sensors that can provide short term measurements. The integration of the two systems can effectively provide continuous navigation data even during GPS signal outages. Traditional INSs are bulky and expensive, and therefore, can not be used for daily civilian applications. With the evolution of MEMS technology, MEMS-based INS sensors are evolving into more accurate, compact and inexpensive units. Hence, there is a growing interest in exploring the capabilities of these sensors in the field of vehicle navigation. Most of the research is targeted towards finding the best error models and integration techniques that can reduce the high drift and errors associated with these sensors. One of the important aspects of this integration is the optimal configuration for sampling frequency, number of bits and time delay during recording of the various sensor outputs. The very low cost of the MEMS sensors makes the cost of the signal sampling, i.e. analog to digital conversion (ADC), an issue. These parameters will reduce the on-board memory requirement, speed up the computation and hence, significantly reduce the final cost to the consumers.