In the dynamic landscape of artificial intelligence and robotics, the pursuit of accurate positioning in mobile robots has intensified. This research addresses the limitations of single-sensor SLAM (Simultaneous Localization and Mapping) techniques in complex settings by harnessing the collective strengths of LiDAR (Light Detection And Ranging), Camera, IMU (Inertial Measurement Unit), and GNSS (Global Navigation Satellite System) sensors. The proposed multi-sensor tightly-coupled SLAM framework is an integration of point-line feature-based laser-visual-inertial odometry, visual-laser fusion loop closure detection, and factor graph-based back-end optimization. Within the visual-inertial subsystem, an advanced LSD (Line Segment Detector) feature extraction strategy is introduced, incorporating point-line fusion to enhance visual line features. Additionally, the laser point cloud is projected onto the camera coordinate system, establishing depth associations with visual attributes. Strengthening the robustness of the visual-inertial subsystem in low-texture environments, camera poses undergo optimization through a sliding-window bundle adjustment method. In the laser-inertial subsystem, IMU preintegration mitigates laser point cloud distortion. Extracting edge and plane features, coupled with frame-to-local-map matching, enhances matching efficiency while streamlining computational intricacies. This amalgamation forms the basis of the laser-visual-inertial odometry fusion system. To overcome the limitations of standalone visual and laser-based loop closure detection, a dual-loop closure method utilizing visual-laser fusion is proposed. Leveraging the DBoW2 bag-of-words model, complemented by temporal-spatial consistency checks, enhances detection efficiency and accuracy. The integration of GNSS factors imparts global constraints for expansive outdoor scenarios. Employing factor graph-based back-end optimization, the refinement of laser-visual-inertial odometry factors, visual-inertial odometry factors, IMU preintegration factors, loop closure factors, and GNSS factors culminates in precise global pose estimation and high-fidelity point cloud maps. Through rigorous evaluation of the M2DGR dataset and a mobile robot platform, the proposed methodology emerges as an exemplar of performance, showcasing superiority over the state-of-the-art LIO-SAM technique. Achieving a reduction of 2.86 m and 3.23 m in the root mean square error of absolute pose estimation across divergent environments, this approach exhibits remarkable efficacy in outdoor scenarios, thereby elevating the precision and resilience of SLAM algorithms for mobile robots.

Blockchain has proven in sensor networks as a distributed solution for transparent and secure storage, which allows its application in mobile wireless sensor networks (MWSNs). The consensus mechanism, an essential aspect of blockchain technology, must concern the high mobility, resource-constrained nature, and weak physical defenses of sensor nodes in MWSNs. To secure MWSN data storage in clustered communication, we design a proof-of-information (PoI) variant for fair miner campaigning via the amount of valid data generated from environmental information, including a dynamic adjustment of the data volume threshold to detect malicious nodes and prevent them from misreporting information. Additionally, we introduce a filtering mechanism through the dynamic integrated trust (DIt) of nodes, which integrates the trust evaluation of peer nodes across the network combining objective performance to prevent malicious nodes from infiltrating the final consensus group. The multi-level filtering technique improves the campaign fairness while isolating malicious nodes, ensuring complexity-sensitive PBFT algorithm efficiency in large-scale networks. Simulation results show that the scheme isolates 90% of the malicious nodes and screens 20% of members to produce a smaller final consensus group. Further analysis of impacts on the performance considering network topology and mobility patterns and comparisons of the relevant solutions are presented.

In machine learning, stochastic gradient descent (SGD) is widely deployed to train models using highly non-convex objectives with equally complex noise models. Unfortunately, SGD theory often makes restrictive assumptions that fail to capture the non-convexity of real problems, and almost entirely ignore the complex noise models that exist in practice. In this work, we demonstrate the restrictiveness of these assumptions using three canonical models in machine learning. Then, we develop novel theory to address this shortcoming in two ways. First, we establish that SGD's iterates will either globally converge to a stationary point or diverge under nearly arbitrary nonconvexity and noise models. Under a slightly more restrictive assumption on the joint behavior of the non-convexity and noise model that generalizes current assumptions in the literature, we show that the objective function cannot diverge, even if the iterates diverge. As a consequence of our results, SGD can be applied to a greater range of stochastic optimization problems with confidence about its global convergence behavior and stability.