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Electronics
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Image Analysis Using LiDAR Data
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Image Analysis Using LiDAR Data

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Circuit and Signal Processing".

Deadline for manuscript submissions: 15 October 2025 | Viewed by 2662

Special Issue Editor

Dr. Yilong Zhu

E-Mail Website
Guest Editor
Department of Electronic and Computer Engineering, Hong Kong University of Science and Technology, Kowloon, Hong Kong
Interests: image; camera; ostdeutscher rundfunk brandenburg; pose estimation; attitude determination; star trackers; Kalman filter; range finders; lidar; calibration

Special Issue Information

Dear Colleagues,

With the integration of LiDAR technology with multisensor fusion, SLAM, visual semantic segmentation, and Bird's Eye View (BEV) technologies, the field of image analysis has made significant progress in handling complex environmental data. These advancements have led to substantial success in applications ranging from autonomous driving to urban planning and robotic navigation. However, the performance of these systems is still limited by the processing power, algorithm optimization, and data quality and diversity. This Special Issue aims to showcase the latest research achievements and developments in image analysis using LiDAR and visual image data combined with the aforementioned technologies.

Topics covered in this Special Issue include, but are not limited to, the following:

  1. 3D reconstruction from LiDAR data;
  2. Multisensor data fusion;
  3. Applications of SLAM technology;
  4. Visual semantic segmentation;
  5. Generation and analysis of Bird's Eye View (BEV);
  6. Real-time navigation and positioning systems;
  7. Environmental monitoring and terrain analysis;
  8. Optimization of autonomous driving algorithms;
  9. Disaster assessment and emergency response;
  10. Datasets related to robotics and unmanned driving;
  11. The use of deep learning technologies like Transformers in robotic perception.

Dr. Yilong Zhu
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • LiDAR
  • multisensor fusion
  • SLAM (simultaneous localization and mapping)
  • visual semantic segmentation
  • bird's eye view (BEV)
  • real-time navigation
  • environmental monitoring
  • robotics data
  • deep learning
  • transformer models

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Published Papers (2 papers)

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Research

24 pages, 5261 KiB  
Article
Extended Study of a Multi-Modal Loop Closure Detection Framework for SLAM Applications
by Mohammed Chghaf, Sergio Rodríguez Flórez and Abdelhafid El Ouardi
Electronics 2025, 14(3), 421; https://doi.org/10.3390/electronics14030421 - 21 Jan 2025
Viewed by 1034
Abstract
Loop Closure (LC) is a crucial task in Simultaneous Localization and Mapping (SLAM) for Autonomous Ground Vehicles (AGV). It is an active research area because it improves global localization efficiency. The consistency of the global map and the accuracy of the AGV’s location [...] Read more.
Loop Closure (LC) is a crucial task in Simultaneous Localization and Mapping (SLAM) for Autonomous Ground Vehicles (AGV). It is an active research area because it improves global localization efficiency. The consistency of the global map and the accuracy of the AGV’s location in an unknown environment are highly correlated with the efficiency and robustness of Loop Closure Detection (LCD), especially when facing environmental changes or data unavailability. We propose to introduce multimodal complementary data to increase the algorithms’ resilience. Various methods using different data sources have been proposed to achieve precise place recognition. However, integrating a multimodal loop-closure fusion process that combines multiple information sources within a SLAM system has been explored less. Additionally, existing multimodal place recognition techniques are often difficult to integrate into existing frameworks. In this paper, we propose a fusion scheme of multiple place recognition methods based on camera and LiDAR data for a robust multimodal LCD. The presented approach uses Similarity-Guided Particle Filtering (SGPF) to identify and verify candidates for loop closure. Based on the ORB-SLAM2 framework, the proposed method uses two perception sensors (camera and LiDAR) under two data representation models for each. Our experiments on both KITTI and a self-collected dataset show that our approach outperforms the state-of-the-art methods in terms of place recognition metrics or localization accuracy metrics. The proposed Multi-Modal Loop Closure (MMLC) framework enhances the robustness and accuracy of AGV’s localization by fusing multiple sensor modalities, ensuring consistent performance across diverse environments. Its real-time operation and early loop closure detection enable timely trajectory corrections, reducing navigation errors and supporting cost-effective deployment with adaptable sensor configurations. Full article
(This article belongs to the Special Issue Image Analysis Using LiDAR Data)
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14 pages, 4975 KiB  
Article
Assessment of Tree Species Classification by Decision Tree Algorithm Using Multiwavelength Airborne Polarimetric LiDAR Data
by Zhong Hu and Songxin Tan
Electronics 2024, 13(22), 4534; https://doi.org/10.3390/electronics13224534 - 19 Nov 2024
Cited by 2 | Viewed by 1170
Abstract
Polarimetric measurement has been proven to be of great importance in various applications, including remote sensing in agriculture and forest. Polarimetric full waveform LiDAR is a relatively new yet valuable active remote sensing tool. This instrument offers the full waveform data and polarimetric [...] Read more.
Polarimetric measurement has been proven to be of great importance in various applications, including remote sensing in agriculture and forest. Polarimetric full waveform LiDAR is a relatively new yet valuable active remote sensing tool. This instrument offers the full waveform data and polarimetric information simultaneously. Current studies have primarily used commercial non-polarimetric LiDAR for tree species classification, either at the dominant species level or at the individual tree level. Many classification approaches combine multiple features, such as tree height, stand width, and crown shape, without utilizing polarimetric information. In this work, a customized Multiwavelength Airborne Polarimetric LiDAR (MAPL) system was developed for field tree measurements. The MAPL is a unique system with unparalleled capabilities in vegetation remote sensing. It features four receiving channels at dual wavelengths and dual polarization: near infrared (NIR) co-polarization, NIR cross-polarization, green (GN) co-polarization, and GN cross-polarization, respectively. Data were collected from several tree species, including coniferous trees (blue spruce, ponderosa pine, and Austrian pine) and deciduous trees (ash and maple). The goal was to improve the target identification ability and detection accuracy. A machine learning (ML) approach, specifically a decision tree, was developed to classify tree species based on the peak reflectance values of the MAPL waveforms. The results indicate a re-substitution error of 3.23% and a k-fold loss error of 5.03% for the 2106 tree samples used in this study. The decision tree method proved to be both accurate and effective, and the classification of new observation data can be performed using the previously trained decision tree, as suggested by both error values. Future research will focus on incorporating additional LiDAR data features, exploring more advanced ML methods, and expanding to other vegetation classification applications. Furthermore, the MAPL data can be fused with data from other sensors to provide augmented reality applications, such as Simultaneous Localization and Mapping (SLAM) and Bird’s Eye View (BEV). Its polarimetric capability will enable target characterization beyond shape and distance. Full article
(This article belongs to the Special Issue Image Analysis Using LiDAR Data)
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