Place recognition and localization using sonar images is a critical task in the underwater Internet of Things (IoT). In this paper, we propose a robust multi-sensor fusion method that fuses mechanical scanning imaging sonar (MSIS), an inertial measurement unit (IMU), and a Doppler velocity log (DVL). To address the sparse and noisy nature of MSIS data, we introduce dedicated feature descriptors based on echo intensity and structural clustering for stable registration. Moreover, we design a double-check loop closure detection scheme that combines complementary algorithms to effectively suppress false positives and reduce missed detections. Experimental evaluations on multiple real-world datasets and the HoloOcean platform demonstrate that the proposed method consistently achieves robust and accurate performance across diverse underwater environments.

Simultaneous localization and mapping (SLAM) has emerged as a promising solution to address the localization challenges faced by underwater robots. This work proposes a factor graph optimization-based Visual-Inertial-Doppler Velocity Log (DVL) tightly-coupled SLAM method designed for underwater robot localization. The approach introduces a novel DVL residual construction method to maximize the utility of DVL measurements. IMU data is integrated to detect anomalies in DVL measurements, and a DVL synchronization marking strategy is developed to enhance the method’s applicability and strengthen data associations. To improve system robustness in scenarios where visual tracking fails, a new sliding window strategy is incorporated. Experimental results on underwater datasets and simulations demonstrate that the proposed method achieves significant improvements in localization accuracy and robustness compared to existing underwater localization algorithms. The implementation code of the proposed method and a simulated underwater dataset can be accessed at https://github. com/uestc-icsp/VID-SLAM.

In the era of Industry 4.0, distributed storage systems face significant challenges in terms of reliability, data security, privacy, and maintenance. While traditional solutions like HDFS rely on centralized nodes that create single points of failure, existing blockchain-based alternatives often lack efficient mechanisms to dynamically evaluate node reliability in untrusted environments, leading to potential data unavailability.This study introduces a secure, blockchain-based distributed storage system using the Trusted Adaptive File Allocation (TAFA) Algorithm for efficient file distribution. The system uses smart contracts to automatically incentivize and monitor nodes, rewarding or penalizing them to boost long-term availability. Extensive simulation experiments demonstrate that this system achieves an optimal Load Balance Factor (LBF) of nearly 1.00 and maintains high storage reliability. It is noteworthy that, even in environments with up to 90% malicious nodes performing tampering or denial-of-service attacks, the file availability of this system remains at least 30%, which is up to 5.7 times higher than that of other distributed storage algorithms.

Simultaneous Localization and Mapping (SLAM) has become a critical technology for intelligent transportation systems and autonomous robots and is widely used in autonomous driving. However, traditional manual feature-based methods in challenging lighting environments make it difficult to ensure robustness and accuracy. Some deep learning-based methods show potential but still have significant drawbacks. To address this problem, we propose a feature-based visual SLAM system based on the LightGlue deep learning network. It uses deep local feature descriptors to replace traditional hand-crafted features and a more efficient and accurate deep network to achieve fast and precise feature matching. Thus, we use the robustness of deep learning to improve the whole system. We have combined traditional geometry-based approaches to introduce a complete visual SLAM system for monocular, binocular, and RGB-D sensors. We thoroughly tested the proposed system on four public datasets: KITTI, EuRoC, TUM, and 4Season, as well as on actual campus scenes. The experimental results show that the proposed method exhibits better accuracy and robustness in adapting to low-light and strongly light-varying environments than traditional manual features and deep learning-based methods. It can also run on GPU in real time.

The remarkable success of deep learning (DL) in predicting battery health has prompted interest in its application in recent years. While state-of-the-art DL models have achieved high accuracy in battery health prediction, they have not been widely adopted in industrial workflows, primarily due to their lack of interpretability and security. To address this issue, we propose a blockchain-based interpretable prediction algorithm for battery health prediction in electric vehicles (EVs) within the Internet of Vehicles (IoV). Specifically, the proposed method includes a platform architecture for a blockchain-based DL system, ensuring secure storage of user data during the prediction process. Notably, we develop a novel battery life prediction algorithm called BLP-Transformer, which leverages short-term relationships between degraded data and explains the impact of feature extraction on predicted results through the contribution of aggregated features based on a feature focusing mechanism. Experimental results demonstrate that the system is feasible for security and can provide accurate battery life prediction. In addition, the comparison study further highlights the superiority of the proposed algorithm in terms of robustness, prediction accuracy, and model interpretability.

Due to the advantages of low-cost, easy use, and high-sensitivity, magnetic sensors are being widely used in parking detection. However, the magnetic signals contain a lot of noise, which can affect the detection performance. In this article, a parking detection algorithm based on a magnetic wireless sensor network is proposed, which adopts the translation-invariant wavelet denoising method to preprocess magnetic signals, aiming to reduce the impact of noise and meet the detection requirements. Considering the problems of weak magnetic vehicles and adjacent vehicle interference in practical applications, a multitransitory finite state machine based on variance signal is designed for parking detection. The experimental results in a standard parking lot show that the proposed algorithm has a significant improvement in accuracy compared with the classical algorithms, and provides theoretical support for building smart parking systems and improving vehicle management efficiency. In addition, the proposed algorithm can also accurately detect the special case of weak magnetic vehicles and has a wider range of applicability.

Visual Simultaneous Localization and Mapping (VSLAM) has undergone gradual development and found widespread application. However, existing VSLAM systems predominantly rely on static environment assumptions, leading to diminished robustness and localization accuracy in the presence of dynamic elements. Previous research has primarily employed geometric and semantic constraints to address dynamic regions of the scene. Nevertheless, their efficacy is limited in complex dynamic scenarios involving non-rigid objects, non-predefined motion targets, and low dynamic motion targets. Furthermore, the majority of dynamic SLAM methods are predominantly designed for indoor RGBD environments, resulting in a lack of generalizability. In this paper, a dynamic SLAM method that combines instance segmentation and optical flow called RSO-SLAM is proposed. RSO-SLAM is designed to operate effectively in diverse complex motion scenarios, both indoors and outdoors, and supports various visual sensor modes, including monocular, stereo, and RGBD setups. The proposed approach amalgamates semantic information and optical flow data by employing a “KMC:k-means$+$connectivity” based algorithm for motion region detection within the scene. Furthermore, it integrates an optical flow attenuation propagation strategy to facilitate meticulous motion probability computations and inter-frame propagation within each identified region. Our methodology’s superiority over existing dynamic SLAM approaches is firmly established through comprehensive evaluations across a diverse range of intricate dynamic scenarios. These evaluations encompass various conditions of high and low dynamism in both indoor and outdoor environments, accompanied by rigorous ablation experiments and real-world assessments. RSO-SLAM exhibits enhanced robustness and higher localization accuracy, rendering it well-suited for nearly all dynamic environments.