In various pedestrian motion scenarios, data-driven pedestrian dead reckoning (PDR) methods have demonstrated strong localization performance, significantly enhancing system adaptability and robustness. However, most existing deep learning models have high computational costs. This makes them difficult to deploy on energy-constrained mobile devices for long-term use. Current lightweight PDR networks adopt a single-input-single-output structure. Although effective in complex dynamics, this design becomes redundant in low-dynamic conditions such as static or slow walking. To address this challenge, we propose a lightweight network architecture named Motion-aware Early-exit Inertial Odometry Network (MEIO-Net). The model adopts a multi-exit design and incorporates a dedicated training scheme. Displacement covariance is used to assess motion difficulty. An early exit mechanism is then introduced to adaptively adjust the inference depth based on dynamic complexity. With a slight improvement in accuracy, MEIO-Net significantly reduces computational cost across multiple datasets. In the self-collected data set, it achieves a 48.9% reduction in FLOPs compared to state-of-the-art lightweight models. In typical simple pedestrian motion scenarios, the computational cost of the proposed model is reduced by 50.5%–80.5%. Ablation studies further confirm that MEIO-Net can flexibly terminate inference according to the intensity of the pedestrian motion. These results demonstrate its strong generalizability and practical potential for real-world deployment. Code and dataset are available at: https://github.com/SunXuehang/MEIO-Net.git.

Accurate positioning and navigation capabilities are essential for Internet of Things (IoT) devices. Event cameras, inspired by biological vision sensors, exhibit robust performance in high-dynamic and low-texture environments and are particularly suitable for IoT applications. However, it faces challenges with accuracy and scale in conventional slow-motion scenarios. Conversely, light detection and ranging (LiDAR) offers high precision in normal motion conditions but degrades significantly under high-dynamic motion. To integrate the advantages of both sensors, this article introduces the EVLINS algorithm, a multisource elastic fusion method based on an extended Kalman filter (EKF). This algorithm combines event-visual-inertial odometry (EVIO), LiDAR-inertial odometry (LIO), and an inertial measurement unit (IMU), utilizing a loosely coupled trajectory layer post-processing technique. This algorithm leverages the robustness of event cameras in highly dynamic environments and the precision of LiDAR in conventional settings, utilizing normalized uncertainty and nonholonomic constraint (NHC) strategies to address LIO’s degradation and EVIO’s accuracy issues. Thorough testing in various indoor and outdoor scenarios with real-world data demonstrates that EVLINS exhibits significantly improved accuracy and robustness compared to both LIO and EVIO algorithms. In large-scale, high-dynamic outdoor environments, EVLINS achieves a 3-D position accuracy of 0.68% over 1333.58 m, improving by 33.21% over LIO and 96.10% over EVIO, which diverged mid-way. In extreme indoor dynamic scenarios, EVLINS reduces maximum position error by 41.55% compared to LIO and improves overall position accuracy by 43.48%, and 22.96% compared to EVIO.

Accurate visual localization is essential in IoT applications, particularly for robotics, autonomous systems, and augmented reality. Traditional feature-based methods struggle with efficiency and robustness against environmental variations. To enhance the robustness of visual localization algorithms against these variations, state-of-the-art (SOTA) methods have incorporated semantic information as an advanced dimension into their models, but still suffer from several shortcomings. These methods often embed semantic information implicitly, which limits their extensibility and interpretability. Moreover, the introduction of some unstable semantic labels may, on the contrary, degrade the localization accuracy. Therefore, modularity, quantization, and filtering semantic labels by their stability become critical. To address these gaps, this article proposes a method that explicitly and quantitatively integrates semantic information through a plug-and-play module. This module scores image-to-image and feature-to-feature correspondences based on semantic similarity and stability, with a particular focus on improving smartphone-based visual localization in high-crowd indoor scenarios. This module is introduced into two key stages of visual hierarchical localization: 1) visual place recognition (coarse localization) and 2) 6-Degree-of-Freedom pose estimation (fine localization). Specifically, correspondences with low scores imply a higher probability of matching errors and are therefore suppressed. To validate the proposed approach, a novel dataset designed for semantic visual localization tasks is collected, rich with dynamic objects and scene variations. The method demonstrates superior accuracy and robustness, particularly in environments with significant scene appearance changes, with 13.6% and 5.4% improvement in localization accuracy in Cafds and Libds datasets, respectively, compared to the SOTA approach. The code and dataset are available athttps://github.com/1da1da/SEVL.

Multisensor integrated navigation, combining the global navigation satellite system (GNSS), inertial navigation system (INS), and light detection and ranging (LiDAR), is at the forefront of high-precision positioning technology. However, existing integration methods rely on raw point cloud data, which cannot be obtained in some projects due to geospatial data privacy concerns. For this problem, this work introduces the trajectory-layer-enhanced GNSS/INS/LiDAR integrated navigation system (TL-GILNS), which operates without raw point cloud data. This novel approach faces two primary challenges: 1) the difficulty in resolving the divergence of LiDAR positioning results due to cumulative errors and 2) the challenge of accurately assigning weights to LiDAR data in the integrated system. To overcome these obstacles, we propose a trajectory-layer LiDAR positioning enhancement method to reduce cumulative errors and a trajectory-layer LiDAR positioning accuracy quantification method to determine the weight of LiDAR. Finally, the performance of TL-GILNS is verified through experiments conducted in semi-open and large-scale complex scenes. In these scenes, TL-GILNS achieves horizontal accuracy better than 0.3 m, vertical accuracy better than 0.7 m, and yaw angle accuracy better than 1°. These results demonstrate the potential of TL-GILNS as a leading approach for high-precision navigation and positioning in complex environments.

As a commonly used indoor Internet of Things (IoT) positioning method, fingerprinting is frequently carried out by fusing inertial and magnetic data. However, magnetic signals may exhibit high similarity in extensive indoor environments, leading to increased mismatching in magnetic fingerprinting positioning results. It negatively impacts the overall accuracy of positioning outcomes, leading to inaccuracies. To address this challenge, this article presents a multilevel error elimination approach that refines magnetic positioning outcomes from coarse to fine-grained adjustments. It marks the inaugural research on error detection and elimination specifically focused on magnetic field positioning. The method employs velocity information, sliding median filtering, and neighborhood filtering to rapidly, accurately, and effectively detect and eliminate the errors of magnetic positioning results. This methodology primarily employs velocity information to rapidly and comprehensively eliminate coarse errors. Subsequently, sliding median filtering addresses scenarios where velocity-based error correction is unreliable, effectively facilitating the intermediate removal of errors in magnetic field positioning outcomes. Ultimately, neighborhood filtering addresses unreliable situations in the above processes, enabling small-scale and detailed elimination of errors in magnetic field positioning results. Within a 100 m$\times $60 m indoor parking lot, the proposed approaches retained 51% to 80% of magnetic positioning results and enhanced the accuracy of magnetic positioning by over 80%.