Purpose Indoor environments often contain repetitive structures, glass surfaces and feature-sparse spaces that degrade LiDAR measurements and impair SLAM performance. To address motion distortion, inertial measurement unit (IMU) drift and loop-closure failures under such conditions, this study aims to develop a robust LiDAR–IMU SLAM framework capable of maintaining stable localization accuracy and constructing globally consistent three-dimensional maps in degraded environments. Design/methodology/approach A tightly coupled LiDAR–IMU SLAM system is established by integrating high-precision spatiotemporal calibration, IMU-driven motion distortion compensation and a multi-dimensional point-cloud descriptor. An adaptive loop-closure detection strategy is introduced using a hybrid histogram of invariant structural characteristics (HISC) descriptor and a dynamic similarity threshold derived from real-time pose analysis. Global pose refinement is achieved through a factor-graph-based optimization model incorporating covariance-weighted constraints and a sliding-window keyframe strategy. The proposed system is evaluated on the KITTI data set and a self-collected indoor data set. Findings Experimental analyses demonstrate that the proposed method improves absolute trajectory accuracy compared with scan context (SC), ISC, A-LiDAR odometry and mapping (LOAM), LeGO-LOAM, LIO-SAM and SC-F-LOAM. The HISC descriptor reduces root mean square absolute trajectory error by up to 15.93% relative to conventional scan-context methods. The adaptive loop-closure module decreases computational time by 60%–78% compared with LIO-SAM while maintaining detection reliability. Mapping experiments show that the system generates geometrically consistent global maps with suppressed drift and improved representation of road structures and indoor spatial features. Originality/value This work introduces a unified LiDAR–IMU fusion framework that tightly couples motion compensation, multimodal global descriptors, adaptive loop-closure validation and factor-graph optimization. The proposed HISC descriptor and dynamic thresholding mechanism enhance loop-closure robustness in repetitive or degenerate environments. The system demonstrates strong adaptability across diverse indoor and urban scenarios, offering a high-precision SLAM solution suitable for long-duration operations and robotics applications requiring reliable localization and mapping.
Purpose This paper aims to present an intelligent micro-electro mechanical system (MEMS) estimator based on an artificial neural network (ANN) for rapid and accurate prediction of performance characteristics of a novel MEMS capacitive pressure sensor featuring a three-layer vacuum-sealed structure with a paraboloid-touched substrate configuration, offering enhanced sensitivity over conventional designs. Design/methodology/approach The sensor is governed by six input parameters, with the ANN simultaneously predicting membrane deflection, output capacitance, capacitive sensitivity and mechanical sensitivity. A four-hidden-layer multilayer perceptron (20 neurons each) is trained on 2,000 COMSOL generated samples, evaluated via fivefold cross-validation and a 10% hold-out test set. All analytical computations were further supported and validated using MATLAB. Findings The proposed sensor achieves an overall capacitive sensitivity of 3.5 & times; 10(-11)F/Pa, with the estimator attaining an overallR(2)of 0.9927 and per-outputR(2)exceeding 0.990 for deflection and mechanical sensitivity. ANN inference time is reduced to a fraction of a second, compared to the considerable time required for direct FEA. Originality/value This work introduces a paraboloid-touched substrate geometry that aligns the capacitor plate with the diaphragm deformation profile at minimum touch pressure, a strategy unexplored in prior touch-mode sensor designs. The machine learning framework, validated through MATLAB-based analytical modeling, offers a scalable and time-efficient alternative to FEA for accelerated MEMS sensor design and optimization.
Purpose-The purpose of this paper is to propose a high-precision self-supervised monocular visual odometry (VO) framework for navigation in complex underwater environments. Design/methodology/approach-An uncertainty-aware underwater image enhancement module is integrated into a self-supervised monocular VO framework. By embedding an uncertainty map estimation branch and fusing features extracted from both pre-enhanced and post-enhanced images, image enhancement and pose estimation are jointly optimized. Furthermore, a coupled cycle consistency loss is introduced to exploit the intrinsic relationship between depth information and underwater transmission maps, thereby improving depth estimation accuracy through mutual supervision. Findings-Experimental results obtained on the publicly available FLSea-VI data set indicate that the proposed method achieves higher depth estimation and pose estimation accuracy than existing state-of-the-art monocular VO approaches. The results further suggest that the proposed framework is well suited to challenging underwater environments. Social implications-The proposed framework may offer incremental safety benefits for diving operations and autonomous navigation. Furthermore, by lowering technical barriers, it could facilitate marine exploration for smaller research teams, thereby encouraging broader participation in marine environmental monitoring and related endeavors. Originality/value-A unified self-supervised underwater visual odometry framework is presented in which image enhancement, depth estimation and pose estimation are jointly optimized. An uncertainty-aware enhancement strategy and a coupled cycle consistency mechanism are introduced to address underwater image degradation and the ill-posed nature of monocular depth estimation.
Purpose This study aims to experimentally validate and characterize PMOS and NMOS current-mirror-integrated cantilever-and bridge-based force sensors. Design/methodology/approach Four sensor configurations were fabricated and characterized, consisting of PMOS- and NMOS-based current-mirror readouts integrated on both cantilever and bridge structures on 2-inch <100> silicon wafers. In each device, the reference MOSFET is placed on the substrate and remains unaffected by applied load, while the output MOSFET, embedded at the fixed edge, converts mechanically induced stress into drain voltage variations via the piezoresistive effect of the MOSFET. Device characterization was performed under controlled and repeatable loading conditions using a nanoindenter. Findings All four sensors showed reproducible voltage responses to the applied force. The sensor sensitivities ranged from 0.010 to 0.95 mV/mN, with the NMOS cantilever sensor showing the highest sensitivity. The NMOS sensors showed approximately an order of magnitude higher sensitivity than PMOS sensors, consistent with the larger piezoresistive coefficients of N-type silicon for the <100> orientation. Furthermore, cantilever sensors showed higher sensitivity than bridge sensors due to their greater mechanical deflection under equivalent loading conditions. Repeated trials and measurements performed on multiple devices showed comparable behavior across the tested samples. Originality/value This work experimentally validates current-mirror-integrated cantilever- and bridge-based force sensors. The proposed approach converts mechanically induced stress directly into an output voltage through a current-mirror circuit, eliminating the need for ΔR/R measurements while maintaining complementary metal-oxide-semiconductor compatibility and reducing readout circuit complexity. The results provide a basis for realizing monolithically integrated piezoMOS force sensing systems, with potential applications in tactile sensing, robotics and biomedical instrumentation.
Purpose With wireless and passive operation as its core technical advantage, this paper aims to systematically review the working principles, technical characteristics and progress in the engineering applications of surface acoustic wave (SAW) sensors, thereby providing a comprehensive reference for further research and the industrialization of wireless passive SAW sensor technology. Design/methodology/approach This work provides a systematic overview of SAW sensing technology for engineering applications, covering practical progress in aerospace engineering, industrial manufacturing, environmental monitoring, transportation engineering and nuclear energy. The discussion considers both the sensing target and its engineering maturity, highlighting temperature, strain, pressure and torque sensing as relatively mature wireless passive directions, while treating gas and humidity sensing as emerging film-functionalized technologies that continue to face challenges in wireless validation, long-term reliability and practical deployment. The review also analyzes how advanced materials, structural optimization and wireless passive technologies improve sensor performance and environmental adaptability, and it identifies the principal technical bottlenecks and prospective development directions. Findings The review clarifies the technical boundaries of wireless passive SAW sensing. Physical sensing targets, including temperature, strain, pressure and torque have reached a relatively mature stage of wireless passive engineering deployment, whereas chemical sensing directions, such as gas and humidity sensing, still face key bottlenecks related to sensitive-film stability, temperature-humidity compensation and acoustic attenuation. Fully passive engineering applications of these chemical sensors therefore remain limited. Other technical constraints include material limitations, packaging challenges and signal interference, while the field is progressing toward greater integration, multiparameter sensing and system-level synergy. Research limitations/implications The review clarifies the technical boundaries of wireless passive SAW sensing. Physical sensing targets, including temperature, strain, pressure, and torque have reached a relatively mature stage of wireless passive engineering deployment, whereas chemical sensing directions, such as gas and humidity sensing, still face key bottlenecks related to sensitive-film stability, temperature-humidity compensation and acoustic attenuation. Fully passive engineering applications of these chemical sensors therefore remain limited. Other technical constraints include material limitations, packaging challenges and signal interference, while the field is progressing toward greater integration, multiparameter sensing and system-level synergy. Originality/value This review systematically examines the development of wireless passive SAW sensors, from their operating principles and multiscenario applications to their technical bottlenecks. It explicitly distinguishes the differences in engineering maturity between physical and chemical sensing and proposes a clear roadmap for future technical research and industrial deployment.
