Jiangbo Song

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 nonline-of-sight (NLOS) 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 m 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.

High-precision and robust autonomous localization is an essential technology for unmanned systems to achieve reliable navigation. Traditional map-based positioning methods can provide high localization accuracy, but as the size of the environment increases, these methods often require significant computational resources and memory, making them unsuitable for resource-constrained platforms. To address this, we propose a lightweight localization method based on sparse maps, designed to reduce computational overhead while maintaining high precision and robustness. Our approach combines LiDAR and inertial measurements by extracting planar features from local regions for scan matching. This method reduces data processing complexity, ensuring both real-time performance and localization accuracy. Unlike traditional methods that match LiDAR data with the global map for every frame, we perform global map matching only at specific intervals, applying pose corrections through a time threshold mechanism, thereby significantly reducing computational burden. Additionally, to improve the accuracy and stability of state estimation, this paper integrates LiDAR data at specific time intervals with the results of map matching, along with planar feature matching results extracted from local regions, to achieve precise pose correction. Combined with a nonlinear geometric observer featuring strong convergence properties, this approach enhances localization accuracy and robustness through multi-sensor fusion. Experimental results on publicly available large-scale datasets and our self-collected datasets demonstrate that our method outperforms existing map-based localization methods by achieving the lowest state update time and reducing computational costs by over 50%, while maintaining robust performance in complex environments. Data will be publicly available on https://github.com/iron2523/sparse-map_localization.

With the widespread adoption of smartphones and IoT technologies, the demand for location-based services (LBSs) 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 ultrawideband (UWB) and the error accumulation in pedestrian dead reckoning (PDR), this article 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 nonoccluded 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 nonoccluded 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 article, 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 intradevice and interdevice 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.

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.

The multipath effect poses an inevitable challenge for Global Navigation Satellite System (GNSS) receivers, particularly pronounced in smart devices, which are nevertheless the most common means through which the general public accesses GNSS positioning. Therefore, we propose a novel adaptive method for extracting multipath errors, which fully exploits the multipath frequency characteristics, multipath repeatability, and the correlation between multipath errors and carrier-to-noise ratio (C/N₀). This approach aims to enhance the susceptibility of smart devices to multipath influences. Specifically, this method uses the code minus carrier method to calculate the noisy multipath error, exploits the correlation between C/N₀ and multipath to constrain the frequency range, employs the ensemble empirical mode decomposition method to decompose the error signal, and utilizes the Hilbert–Huang transform method to obtain the frequency information of the signal component. In multipath repeatability, the proposed method can effectively improve the correlation of multipath signals by up to 0.79. By applying the extraction method, the positioning performance of smart devices can be improved by 13.9%. Therefore, the proposed method extracts multipath error more accurately and is highly usable in low-cost devices.