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Progress in LiDAR Technologies and Applications

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Optical Sensors".

Deadline for manuscript submissions: 25 October 2026 | Viewed by 7140

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Special Issue Editors

Dr. Santiago Royo

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Guest Editor

1. Centre for Sensors, Instrumentation and Systems (CD6), Universitat Politènnica de Catalunya (UPC), Rambla Sant Nebridi 10, E08222 Terrassa, Barcelona, Spain
2. Beamagine S.L., C/Bellesguard 16, E08755 Castellbisbal, Barcelona, Spain
Interests: TOF lidar; FMCW lidar; point cloud processing; industrial applications of lidar
Special Issues, Collections and Topics in MDPI journals

Dr. Maria Ballesta-Garcia

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Guest Editor

Centre de Desenvolupament de Sensors, Instrumentació i Sistemes, Universitat Politècnica de Catalunya, Barcelona, Spain
Interests: lidar modelling; scattering media; polarization

Dr. Pablo García-Gómez

E-Mail Website
Guest Editor

Beamagine S.L., E08755 Castellbisbal, Spain
Interests: lidar; data fusion; point cloud processing; autonomous vehicles

Special Issue Information

Dear Colleagues,

Lidar sensors are currently expending their capabilities at a very fast pace, partly due to technological advances in components and systems, to new measurement methodologies, and to the implementation of computer vision techniques to generalize point cloud processing, including data fusion and novel deep learning networks.

Within this Special Issue, we intend to provide an up-to-date, broad view of the different applications and methods currently expanding the capabilities of lidar sensing in the real world. From innovative applications where lidar was formerly not commonplace (e.g. construction, railway, autonomous vehicles) to improved processing techniques based in AI or data fusion, we intend to implement a picture of the expansion of lidar technologies to different real-world realms and the methods that allow it.

Novel hardware and software approaches enabling applied use cases for lidar are particularly encouraged, both for direct Time-of-Flight (TOF) lidar, amplitude modulated-cameras (AMCW), and frequency-modulated (FMCW) lidar sensors. As an example, topics expected may include point cloud processing strategies including neural networks or deep learning, construction and operation of digital twins based on lidar and data fusion, or radiometric models pushing the state of the art of lidar techniques (e.g. in bad weather or scattering media, underwater, etc.). In all cases, however, they should provide realistic progress beyond the state of the art regarding the implementation of lidar technologies in real-world applications within different industries.

Dr. Santiago Royo
Dr. Maria Ballesta-Garcia
Dr. Pablo García-Gómez
Guest Editors

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Keywords

lidar
TOF lidar
FMCW lidar
AMCW lidar
data fusion
point cloud processing
3D computer vision
scattering media
modelling
autonomous vehicles

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Published Papers (5 papers)

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Research

13 pages, 3501 KB

Open AccessArticle

AWG-Based Spectral Multiplexing for Unambiguous Range-Extended FMCW LiDAR

by Sangwon Park, Sang Min Park, Seongmun Jeong, Gyeongmin Kweon, Chang-Seok Kim and Hwidon Lee

Sensors 2026, 26(5), 1435; https://doi.org/10.3390/s26051435 - 25 Feb 2026

Viewed by 528

Abstract

Frequency-modulated continuous-wave (FMCW) light detection and ranging (LiDAR) based on coherent ranging is a technology capable of high-resolution distance measurement while remaining robust against ambient light interference. However, extending the measurable range remains challenging due to (i) the coherence length limitation of the laser and (ii) distance ambiguity caused by frequency ambiguity in coherent detection. To overcome these limitations, we propose an unambiguous range-extended FMCW LiDAR enabled by arrayed waveguide grating (AWG)-based spectral multiplexing. By spectrally demultiplexing the reference arm into four wavelength channels with sequentially designed optical path delays, multiple independent interference signals are obtained simultaneously without increasing the number of photodetectors or optical couplers. A channel-pair-based distance decoding algorithm is further introduced to resolve distance ambiguity by classifying detection outcomes across adjacent channels and selectively applying predefined operations. The proposed FMCW LiDAR system effectively extends the measurable range to approximately five times that of a conventional FMCW LiDAR. Experimental results demonstrate high measurement accuracy and successful reconstruction of three-dimensional distance maps, validating the system’s potential for extended-range FMCW LiDAR applications. Full article

(This article belongs to the Special Issue Progress in LiDAR Technologies and Applications)

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23 pages, 15010 KB

Open AccessArticle

Hybrid Mamba–Graph Fusion with Multi-Stage Pseudo-Label Refinement for Semi-Supervised Hyperspectral–LiDAR Classification

by Khanzada Muzammil Hussain, Keyun Zhao, Sachal Perviaz and Ying Li

Sensors 2026, 26(3), 1005; https://doi.org/10.3390/s26031005 - 3 Feb 2026

Viewed by 697

Abstract

Semi-supervised joint classification of Hyperspectral Images (HSIs) and LiDAR-derived Digital Surface Models (DSMs) remains challenging due to scarcity of labeled pixels, strong intra-class variability, and the heterogeneous nature of spectral and elevation features. In this work, we propose a Hybrid Mamba–Graph Fusion Network (HMGF-Net) with Multi-Stage Pseudo-Label Refinement (MS-PLR) for semi-supervised hyperspectral–LiDAR classification. The framework employs a spectral–spatial HSI backbone combining 3D–2D convolutions, a compact LiDAR CNN encoder, Mamba-style state-space sequence blocks for long-range spectral and cross-modal dependency modeling, and a graph fusion module that propagates information over a heterogeneous pixel graph. Semi-supervised learning is realized via a three-stage pseudolabeling pipeline that progressively filters, smooths, and re-weights pseudolabels based on prediction confidence, spatial–spectral consistency, and graph neighborhood agreement. We validate HMGF-Net on three benchmark hyperspectral–LiDAR datasets. Compared with a set of eight state-of-the-art (SOTA) baselines, including 3D-CNNs, SSRN, HybridSN, transformer-based models such as SpectralFormer, multimodal CNN–GCN fusion networks, and recent semi-supervised methods, the proposed approach delivers consistent gains in overall accuracy, average accuracy, and Cohen’s kappa, especially in low-label regimes (10% labeled pixels). The results highlight that the synergy between sequence modeling and graph reasoning in combination with carefully designed pseudolabel refinement is essential to maximizing the benefit of abundant unlabeled samples in multimodal remote sensing scenarios. Full article

(This article belongs to the Special Issue Progress in LiDAR Technologies and Applications)

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19 pages, 9525 KB

Open AccessArticle

Evaluating UAV and Handheld LiDAR Point Clouds for Radiative Transfer Modeling Using a Voxel-Based Point Density Proxy

by Takumi Fujiwara, Naoko Miura, Hiroki Naito and Fumiki Hosoi

Sensors 2026, 26(2), 590; https://doi.org/10.3390/s26020590 - 15 Jan 2026

Viewed by 674

Abstract

The potential of UAV-based LiDAR (UAV-LiDAR) and handheld LiDAR scanners (HLSs) for forest radiative transfer models (RTMs) was evaluated using a Voxel-Based Point Density Proxy (VPDP) as a diagnostic tool in a Larix kaempferi forest. Structural analysis-computed coverage gap ratio (CGR) revealed distinct behaviors. UAV-LiDARs effectively captured canopy structures (10–45% CGR), whereas HLS provided superior understory coverage, but exhibited a high upper-canopy CGR (>40%). Integrating datasets reduced the CGR to below 10%, demonstrating strong complementarity. Radiative transfer simulations correlated well with Sentinel-2 NIR reflectance, with the UAV-LiDAR (

r

= 0.73–0.75) outperforming the HLS (

r

= 0.64–0.69). These results highlight the critical importance of upper-canopy modeling for nadir-viewing sensors. Although integrating HLS data did not improve correlation due to the dominance of upper-canopy signals, structural analysis confirmed that fusion is essential for achieving volumetric completeness. A voxel size range of 50–100 cm was identified as effective for balancing structural detail and radiative stability. These findings provide practical guidelines for selecting and integrating LiDAR platforms in forest monitoring, emphasizing that while aerial sensors suffice for top-of-canopy reflectance, multi-platform fusion is requisite for full 3D structural characterization. Full article

(This article belongs to the Special Issue Progress in LiDAR Technologies and Applications)

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24 pages, 5906 KB

Open AccessArticle

Design and Framework of Non-Intrusive Spatial System for Child Behavior Support in Domestic Environments

by Da-Un Yoo, Jeannie Kang and Sung-Min Park

Sensors 2025, 25(17), 5257; https://doi.org/10.3390/s25175257 - 23 Aug 2025

Cited by 1 | Viewed by 1640

Abstract

This paper proposes a structured design framework and system architecture for a non-intrusive spatial system aimed at supporting child behavior in everyday domestic environments. Rooted in ethical considerations, our approach defines four core behavior-guided design strategies: routine recovery, emotion-responsive adjustment, behavioral transition induction, and external linkage. Each strategy is meticulously translated into a detailed system logic that outlines input conditions, trigger thresholds, and feedback outputs, designed for implementability with ambient sensing technologies. Through a comparative conceptual analysis of three sensing configurations—low-resolution LiDARs, mmWave radars, and environmental sensors—we evaluate their suitability based on technical feasibility, spatial integration, operationalized privacy metrics, and ethical alignment. Supported by preliminary technical observations from lab-based sensor tests, low-resolution LiDAR emerges as the most balanced option for its ability to offer sufficient behavioral insight while enabling edge-based local processing, robustly protecting privacy, and maintaining compatibility with compact residential settings. Based on this, we present a working three-layered system architecture emphasizing edge processing and minimal-intrusion feedback mechanisms. While this paper primarily focuses on the framework and design aspects, we also outline a concrete pilot implementation plan tailored for small-scale home environments, detailing future empirical validation steps for system effectiveness and user acceptance. This structured design logic and pilot framework lays a crucial foundation for future applications in diverse residential and care contexts, facilitating longitudinal observation of behavioral patterns and iterative refinement through lived feedback. Ultimately, this work contributes to the broader discourse on how technology can ethically and developmentally support children’s autonomy and well-being, moving beyond surveillance to enable subtle, ambient, and socially responsible spatial interactions attuned to children’s everyday lives. Full article

(This article belongs to the Special Issue Progress in LiDAR Technologies and Applications)

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25 pages, 3655 KB

Open AccessArticle

A Multi-Sensor Fusion Approach Combined with RandLA-Net for Large-Scale Point Cloud Segmentation in Power Grid Scenario

by Tianyi Li, Shuanglin Li, Zihan Xu, Nizar Faisal Alkayem, Qiao Bao and Qiang Wang

Sensors 2025, 25(11), 3350; https://doi.org/10.3390/s25113350 - 26 May 2025

Cited by 2 | Viewed by 2731

Abstract

With the continuous expansion of power grids, traditional manual inspection methods face numerous challenges, including low efficiency, high costs, and significant safety risks. As critical infrastructure in power transmission systems, power grid towers require intelligent recognition and monitoring to ensure the reliable and stable operation of power grids. However, existing methods struggle with accuracy and efficiency when processing large-scale point cloud data in complex environments. To address these challenges, this paper presents a comprehensive approach combining multi-sensor fusion and deep learning for power grid tower recognition. A data acquisition scheme that integrates LiDAR and a binocular depth camera, implementing the FAST-LIO algorithm, is proposed to achieve the spatiotemporal synchronization and fusion of sensor data. This integration enables the construction of a colored point cloud dataset with rich visual and geometric features. Based on the RandLA-Net framework, an efficient processing method for large-scale point cloud segmentation is developed and optimized explicitly for power grid tower scenarios. Experimental validation demonstrates that the proposed method achieves 90.8% precision in tower body recognition and maintains robust performance under various environmental conditions. The proposed approach successfully processes point cloud data containing over ten million points while effectively handling challenges such as uneven point distribution and environmental interference. These results validate the reliability of the proposed method in providing technical support for intelligent inspection and the management of power grid infrastructure. Full article

(This article belongs to the Special Issue Progress in LiDAR Technologies and Applications)

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