In scenarios with insufficient structural features, LiDAR-based SLAM may suffer from degeneracy, resulting in impaired robot localization and mapping and potentially leading to subsequent deviant navigation tasks. Therefore, it is crucial to develop advanced algorithms and techniques to mitigate the degeneracy issue and ensure the robustness and accuracy of LiDAR-based SLAM. This paper presents a LiDAR–inertial simultaneous localization and mapping (SLAM) method based on a virtual inertial navigation system (VINS) to address the issue of degeneracy. We classified different gaits and match each gait to its corresponding torso inertial measurement unit (IMU) sensor to construct virtual foot inertial navigation components. By combining an inertial navigation system (INS) with zero-velocity updates (ZUPTs), we formed the VINS to achieve real-time estimation and correction. Finally, the corrected pose estimation was input to the IMU odometry calculation procedure to further refine the localization and mapping results. To evaluate the effectiveness of our proposed VINS method in degenerate environments, we conducted experiments in three typical scenarios. The results demonstrate the high suitability and accuracy of the proposed method in degenerate scenes and show an improvement in the point clouds mapping effect. The algorithm’s versatility is emphasized by its wide applicability on GPU platforms, including quadruped robots and human wearable devices. This broader potential range of applications extends to other related fields such as autonomous driving.

The safety of composite insulators in high-voltage transmission lines is directly related to the stable operation of the power system, which is a fundamental condition for the normal functioning of people’s lives and industrial production. Composite insulators are exposed to outdoor conditions for extended periods of time, and with the increase in service life, they are subjected to aging due to external environmental factors and electrical stresses. This aging leads to a decline in their electrical insulation, mechanical properties, and other performance, which, in severe cases, may result in power system failures. Therefore, accurate assessment and detection of the aging status of composite insulators are particularly important. Traditional detection methods such as visual inspection, hardness testing, and hydrophobicity testing have limitations, including single functionality and susceptibility to environmental interference, which cannot comprehensively and accurately reflect the aging condition of the insulators. In recent years, spectroscopy-based detection technologies have been increasingly applied for the rapid detection of composite insulators due to their advantages, such as high sensitivity, non-contact measurement, and multi-dimensional information extraction. Common spectroscopic detection methods include Ultraviolet Discharge (UV Discharge), Fourier Transform Infrared (FTIR) Spectroscopy, Raman Spectroscopy (RS), Hyperspectral Imaging (HSI), Laser-Induced Breakdown Spectroscopy (LIBS), and Terahertz (THz) Spectroscopy. These methods offer non-contact, remote, and rapid capabilities, enabling detailed analysis of the insulator’s surface microstructure, chemical composition, and aging characteristics. This paper introduces = spectroscopy-based methods for detecting the aging status of composite insulators, analyzing the advantages and limitations of these methods, and discussing the challenges of their industrial application. Furthermore, the paper reviews the research progress and practical applications of spectroscopic techniques in the evaluation of insulator aging status, systematically summarizing important achievements in the field and providing an outlook for future developments.

In this paper, we propose a novel visual–inertial simultaneous localization and mapping (SLAM) method for intelligent navigation systems that aims to overcome the challenges posed by dynamic or large-scale outdoor environments. Our approach constructs a visual–inertial navigation system by utilizing virtual inertial sensor components that are mapped to the torso IMU under different gait patterns through gait classification. We apply a zero-velocity update (ZUPT) to initialize the system with the original visual–inertial information. The pose information is then iteratively updated through nonlinear least squares optimization, incorporating additional constraints from the ZUPT to improve the accuracy of the system’s positioning and mapping capabilities in degenerate environments. Finally, the corrected pose information is fed into the solution. We evaluate the performance of our proposed SLAM method in three typical environments, demonstrating its applicability and high precision across various scenarios. Our method represents a significant advancement in the field of intelligent navigation systems and offers a promising solution to the challenges posed by degenerate environments.

Focused ultrasound (FU) technology is extensively employed in clinical applications such as tumor ablation, Parkinson’s disease treatment, and neuropathic pain management. The safety and efficacy of FU therapy critically depend on the accurate quantification of the acoustic field, particularly the high-pressure distribution in focal region. To address the limitations of existing acoustic measurement techniques—including invasiveness, inability to measure high sound pressure, and system complexity—this study proposes a non-invasive method termed Laser Deflection Acoustic Field Quantification (LDAQ), based on the laser deflection principle. An experimental system was constructed utilizing the acousto-optic deflection effect, which incorporates precision displacement control, rotational scanning, and synchronized triggering. Through tomographic scanning, laser deflection images of the acoustic field were acquired at multiple orientations. An inversion algorithm using Radon transforms was proposed to reconstruct the refractive index gradient distributions from the variations of light intensity and spot displacement. An adaptive weighted fusion strategy was then employed to map these optical signals to the sound pressure field. To validate the LDAQ technique, an acoustic field generated by an FU transducer operating at 0.84 MHz was measured. The reconstructed results were compared with both hydrophone measurements and numerical simulations. The findings demonstrated high consistency among all three results within the focal zone. Full-field analysis yielded a root mean square error (RMSE) of 0.1102 between LDAQ and simulation, and an RMSE of 0.1422 between LDAQ and hydrophone measurements. These results confirm that LDAQ enables non-invasive and high-precision quantification of megapascal-level focused acoustic fields, offering a reliable methodology for acoustic field characterization to support FU treatment optimization and device standardization.