Currently, the acquisition and application of multimodal sensing information are deeply integrated with artificial intelligence, becoming a leading hotspot in the field of intelligent perception. With the help of AI, especially deep learning methods, massive and heterogeneous sensor data (such as LiDAR point clouds, spectral images, radar signals) are no longer passive collected physical quantities [1–5], but have become the core assets driving intelligent decision-making. AI endows multimodal fusion with powerful analytical capabilities, enabling the extraction of high-dimensional features from complex environments and achieving more accurate target recognition, situation awareness, and prediction [6–9]. In fields such as smart cities, autonomous driving, and industrial Internet of Things, this combination not only breaks through the perception limitations of a single sensor, but also promotes the system’s transition from perception to cognition, providing key technical support for achieving autonomous intelligence and adaptive control. This Research Topic- Volume III comprises several original research and review works, which are summarized below:
For the gas sensing, Peng et al. presents a compact, highly sensitive photoacoustic spectroscopy sensor for dissolved acetylene detection in transformer oil. Using a 1.53 μm DFB laser and optimized wavelength modulation, the system achieves excellent linearity and long-term stability. It demonstrates a detection limit of 50 ppb, enabling accurate, real-time fault diagnosis and early warning for transformers. Chang et al. reviews Quartz-Enhanced Photoacoustic Spectroscopy (QEPAS) for trace gas detection. It elaborates on the fundamental principle and summarizes progress in laser sources and quartz tuning fork optimization for enhanced sensitivity. Auxiliary strategies like relaxants and waveguides are evaluated. The review concludes by addressing current technical bottlenecks and proposing future prospects for this compact, high-sensitivity sensing technology. Qian et al. reviews gas detection technologies for lithium-ion battery thermal runaway, a critical safety risk. It details the mechanisms of gas generation and critically evaluates techniques like gas chromatography and optical sensors. Special focus is given to optical fiber sensors for their EMI immunity and high sensitivity. The review concludes by discussing current limitations and proposing future directions for integrated, cost-effective battery safety monitoring.
For LiDAR, Cao et al. presents an innovative point cloud down-sampling method using Fuzzy C-Means clustering. By analyzing X, Y, Z coordinates separately and integrating membership functions, it calculates importance scores for adaptive sampling. This approach preserves critical geometric features better than traditional methods while removing redundant data. It shows strong potential for real-time applications in autonomous driving and 3D reconstruction. Zhang et al. developed an airborne LiDAR system for high-speed flight platforms (up to 120 km/h). Integrating dual-wavelength laser and MEMS scanning, it achieves high-precision calibration and real-time power line recognition using improved neural networks. The system also performs rapid terrain assessment via RANSAC at 5 ms per scan. This provides an effective solution for enhancing flight safety through real-time obstacle detection. Also, Zhang et al. analyzes a 635 nm laser stability via multi-physics modeling. Results show ambient heat causes grating non-uniformity, structural deformation, and wavelength drift of 0.191 nm. The analysis clarifies dominant instability mechanisms and guides optimization for precision applications.
Liu et al. presents a novel method for optical fiber plane curve reconstruction using cubic spline interpolation and tangent angle recursion. Strain data from sensors yields discrete curvature values, which are smoothed via interpolation. Coordinates are then calculated recursively for precise reconstruction. With 50 sampling points, mean absolute error reaches 0.000892 m, significantly outperforming lower sampling rates and validating the method’s feasibility. Liu et al. presents a broadband metasurface antenna designed using characteristic mode analysis. By loading parasitic patches and etching slots based on mode optimization, the antenna achieves improved impedance matching and enhanced high-frequency gain. The optimized higher-order modes exhibit broadside radiation, significantly boosting realized gain. This CMA-based approach provides an effective strategy for designing high-performance, flat-gain antennas.
This Research Topic brings together cutting-edge research on the acquisition and application of multimodal sensing information, fully reflecting the development trend of deep integration with artificial intelligence. In the field of gas sensing, researchers have developed a photoacoustic spectroscopy-based sensor for dissolved acetylene detection in transformer oil, achieving a high sensitivity of 50 ppb. Systematic reviews are provided on the progress of quartz-enhanced photoacoustic spectroscopy (QEPAS) and gas monitoring technologies for lithium-ion battery thermal runaway, with special emphasis on the electromagnetic interference immunity of optical fiber sensors. In the field of LiDAR, an innovative point cloud down-sampling method based on fuzzy C-means clustering was proposed to effectively preserve geometric features. An airborne LiDAR system suitable for high-speed flight platforms up to 120 km/h was developed, enabling real-time power line recognition and terrain assessment. Furthermore, research was conducted on accuracy optimization for optical fiber curve reconstruction and the design of a broadband metasurface antenna based on characteristic mode analysis. These achievements demonstrate a transition from single sensors to multimodal fusion, and from data acquisition to intelligent cognition, providing key technical support for applications such as autonomous driving, smart cities, and the industrial Internet of Things.
Statements
Author contributions
XY: Writing – original draft. CJ: Writing – review and editing. HZ: Writing – review and editing. AS: Writing – review and editing. KX: Writing – review and editing.
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The author(s) declared that financial support was not received for this work and/or its publication.
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Summary
Keywords
3D geospatial sensing, laser spectroscopy, LiDAR point cloud processing, optical sensor, signal processing
Citation
Yin X, Jiang C, Zheng H, Sampaolo A and Xu K (2026) Editorial: Acquisition and application of multimodal sensing information, volume III. Front. Phys. 14:1849264. doi: 10.3389/fphy.2026.1849264
Received
07 April 2026
Accepted
10 April 2026
Published
28 April 2026
Volume
14 - 2026
Edited and reviewed by
Antonio Riveiro Rodriguez, University of Vigo, Spain
Updates
Copyright
© 2026 Yin, Jiang, Zheng, Sampaolo and Xu.
This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Xukun Yin, xkyin@xidian.edu.cn
Disclaimer
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.