Global navigation satellite system (GNSS) provides precise, real-time, all-weather location services for Internet of Things (IoT) applications. However, smartphone positioning accuracy is easily affected by signal obstructions such as multipath and non-line-of-sight effects in challenging urban environments. Therefore, we present a bidirectional long short-term memory (BiLSTM) neural network capable of learning the spatiotemporal mechanisms in positioning to mitigate precise point positioning (PPP) errors. We derive the relationships between time, velocity, displacement, and position in GNSS positioning and introduce the concepts of instantaneous displacement error, cumulative displacement error, and estimated positioning error as pseudo-error variables. Based on the spatiotemporal variation mechanisms, we construct a BiLSTM network to learn error patterns in smartphone PPP technology to improve positioning accuracy. Validated on the Google Smartphone Decimeter Challenge (GSDC) dataset, our method reduces positioning errors by 50–75%, thereby achieving a positioning accuracy of 2–3 meters in three directions in challenging urban environments. Compared to other advanced learning-based positioning methods, our approach demonstrates strong versatility under data-constrained conditions, on unseen devices, and across untrained trajectories, while maintaining comparable positioning accuracy. Thus, the proposed mechanisms and methods have the potential to be applied to various ubiquitous global positioning applications.

With the widespread adoption of smartphones and IoT technologies, the demand for location-based services has grown significantly. However, positioning accuracy often fails to meet application requirements due to signal interference from architectural obstructions and other factors. To address the non-line-of-sight (NLOS) errors in ultra-wideband (UWB) and the error accumulation in pedestrian dead reckoning (PDR), this paper proposes a tightly coupled PDR/UWB indoor positioning method with NLOS adaptive correction. By integrating extended Kalman filtering (EKF), the method eliminates pedestrian height parameters through coplanar base station projection and adaptively corrects NLOS ranging errors using chi-square testing. Experimental validation in both non-occluded and occluded underground parking environments demonstrates that, compared to standalone UWB positioning, the loosely coupled and tightly coupled algorithms improve positioning accuracy by 41.28% and 44.84% in non-occluded conditions and by 36.52% and 42.89% in occluded conditions, respectively. The results indicate that the tightly coupled algorithm achieves higher precision and exhibits superior robustness in complex environments, providing an effective solution for high-accuracy indoor positioning.

Reliable indoor localization is crucial for location-based services.Unlike outdoor environments where the Global Navigation Satellite System (GNSS) is prevalent, indoor localization systems employ diverse methods to enhance the accuracy of individual devices. However, these methods face limitations, such as the dependence on pre-existing map data and the necessity of installing anchors. The advancement of the Internet of Things (IoT) and the increasing availability of smart devices have enabled the development of more flexible and dynamic indoor localization solutions. In this paper, we propose a novel method to enhance indoor localization through cooperative localization framework. The core concept involves utilizing existing robots as mobile robot anchors to enhance pedestrian localization accuracy through interaction with pedestrians, particularly in environments lacking fixed anchors. We employed a factor graph optimization approach to tightly couple intra-device and inter-device data. This integration dynamically adjusts the inclusion of anchor data based on its quality, thereby minimizing error propagation. The experimental results demonstrate that the localization accuracy of our proposed method better than extend Kalman filter algorithms, emphasizing the potential of mobile IoT devices in indoor localization systems.

Visual-Inertial Odometry (VIO) often suffers from drifting, particularly in large-scale environments. Concurrently, the Global Navigation Satellite System (GNSS) signals will be intermittent or even inaccessible in obstructed environments. In this work, we present a robust, real-time GNSS-visual-inertial state estimator, abbreviated as R-GVIO, that achieves drift-free six-degree-of-freedom accurate global positioning in urban challenging environments. Specifically, we integrate GNSS into the optimization-based monocular and stereo VIO, enabling the fusion of GNSS Real-time Kinematic (RTK), reprojection error, and IMU pre-integration within a factor graph framework. In addition, to enhance the R-GVIO’s robustness in GNSS-unfriendly environments, we propose an online GNSS measurement outlier detection and culling algorithm based on velocity constraints and a time synchronization mechanism. Furthermore, we propose a GNSS-aided initialization method that provides accurate gravity direction, IMU zero bias, and local-global external parameters. Moreover, we adopt the marginalization strategy of covisibility graph keyframes and add the GNSS factor constraint on the covisibility keyframe pose, ensuring the real-time performance of the system. Experiments on public datasets and real-world experiments verify that in challenging environments, including urban canyons, complex indoor-outdoor, and large-scale environments, the positioning accuracy and robustness of the proposed R-GVIO algorithm are superior to the state-of-the-art algorithms VINS-Fusion, ORB-SLAM3, IC-GVINS, and GICI-LIB. To make contribute to the community, we open sourced the complex environment datasets “SYSU-Campus-GVI” on GitHub.

Presents corrections to the paper, (Corrections to “R₂-GVIO: A Robust, Real-Time GNSS-Visual-Inertial State Estimator in Urban Challenging Environments”).

Visual-inertial odometry (VIO) has played an important role in the field of the Internet of Things, providing a variety of devices and systems with high-precision and reliable positioning and navigation capabilities. In particular, indoor environments have become an important scenario for its application. However, the lack of robustness of point-based VIO systems in low-textured man-made environments often leads to failure. Based on this issue, this article proposes an innovative monocular VIO approach to fully utilize the available information in man-made environments. In the front end, the inertial measurement unit measurement model is defined by the preintegration method. In image data, first, the line features undergo a uniformization process, which reduces the redundant features and improves the accuracy of line feature matching. Then, the vanishing points in the image are detected using the Manhattan world assumption, and the structural line features are classified as either parallel or perpendicular to gravity based on vanishing points. In the back end, a novel residual term is defined for structural line features and gravity, deriving the corresponding Jacobian. This approach effectively addresses the issue of structural line degeneracy and continuously optimizes gravity, while also increasing the utilization of structural lines. A sliding window nonlinear optimization method is employed to minimize the sum of residuals. We tested the proposed system and the state-of-the-art VIO systems on both the public data sets and our collected data set to validate the effectiveness of the proposed system.