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Remote Sensing
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LiDAR Technology for Autonomous Navigation and Mapping
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LiDAR Technology for Autonomous Navigation and Mapping

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Engineering Remote Sensing".

Deadline for manuscript submissions: 28 August 2025 | Viewed by 694

Special Issue Editors

Dr. Yiqing Yao

E-Mail Website
Guest Editor
School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China
Interests: LiDAR; inertial navigation; integration systems; multi-sensor data fusion scheme; polar navigation
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Johnson Ihyeh Agbinya

E-Mail Website
Guest Editor
School of Information Technology and Engineering, Melbourne Institute of Technology, 288 Latrobe Street, Melbourne, VIC 3000, Australia
Interests: remote sensing; sensors; smart environments; deep learning; IoT; radar (LiDAR); wireless power transfer
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Autonomous navigation and mapping has always been a prerequisite and essential foundation for autonomous systems, providing location and environmental information for decision making and vehicle operation. In recent years, LiDAR systems have demonstrated tremendous potential for enhancing perception capabilities in autonomous navigation and mapping tasks. Autonomous vehicles, ships, and drones, for instance, rely heavily on LiDAR for safe and efficient navigation, particularly in GNSS-denied and challenged environments such as indoors, underground, and in urban canyon areas. Quantities of corresponding algorithms and methodologies are proposed, which may rely solely on LiDAR or integrate data from multiple sources (e.g., inertial measurement units, cameras, or radars). Furthermore, due to LiDAR’s high ranging accuracy and its ability to provide detailed point cloud data, it offers significant advantages for mapping and environmental perception, laying the groundwork for subsequent tasks such as path planning and mission execution.

This Special Issue aims to explore innovative research on the application of LiDAR technology in autonomous navigation and mapping. Topics may involve positioning, detection, or mapping using LiDAR-based methods or multi-sensor fusion solutions in various environments, including indoors and in urban settings. The Special Issue topics include but are not limited to the following:

Autonomous navigation solutions (e.g., odometry, simultaneous localization and mapping);

Autonomous detection and avoidance (e.g., for targets or obstacles);

Mapping using LiDAR point clouds;

LiDAR applications in GNSS-denied environments;

LiDAR-based multi-sensor fusion systems;

AI in LiDAR data processing;

Autonomous decision-making models using LiDAR.

Dr. Yiqing Yao
Prof. Dr. Johnson Ihyeh Agbinya
Guest Editors

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. Remote Sensing 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 2700 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 odometry
  • simultaneous localization and mapping
  • obstacle detection and avoidance
  • 3D mapping
  • multi-sensor fusion
  • autonomous vehicles

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Published Papers (1 paper)

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Research

20 pages, 3710 KiB  
Article
An Accurate LiDAR-Inertial SLAM Based on Multi-Category Feature Extraction and Matching
by Nuo Li, Yiqing Yao, Xiaosu Xu, Shuai Zhou and Taihong Yang
Remote Sens. 2025, 17(14), 2425; https://doi.org/10.3390/rs17142425 - 12 Jul 2025
Viewed by 317
Abstract
Light Detection and Ranging(LiDAR)-inertial simultaneous localization and mapping (SLAM) is a critical component in multi-sensor autonomous navigation systems, providing both accurate pose estimation and detailed environmental understanding. Despite its importance, existing optimization-based LiDAR-inertial SLAM methods often face key limitations: unreliable feature extraction, sensitivity [...] Read more.
Light Detection and Ranging(LiDAR)-inertial simultaneous localization and mapping (SLAM) is a critical component in multi-sensor autonomous navigation systems, providing both accurate pose estimation and detailed environmental understanding. Despite its importance, existing optimization-based LiDAR-inertial SLAM methods often face key limitations: unreliable feature extraction, sensitivity to noise and sparsity, and the inclusion of redundant or low-quality feature correspondences. These weaknesses hinder their performance in complex or dynamic environments and fail to meet the reliability requirements of autonomous systems. To overcome these challenges, we propose a novel and accurate LiDAR-inertial SLAM framework with three major contributions. First, we employ a robust multi-category feature extraction method based on principal component analysis (PCA), which effectively filters out noisy and weakly structured points, ensuring stable feature representation. Second, to suppress outlier correspondences and enhance pose estimation reliability, we introduce a coarse-to-fine two-stage feature correspondence selection strategy that evaluates geometric consistency and structural contribution. Third, we develop an adaptive weighted pose estimation scheme that considers both distance and directional consistency, improving the robustness of feature matching under varying scene conditions. These components are jointly optimized within a sliding-window-based factor graph, integrating LiDAR feature factors, IMU pre-integration, and loop closure constraints. Extensive experiments on public datasets (KITTI, M2DGR) and a custom-collected dataset validate the proposed method’s effectiveness. Results show that our system consistently outperforms state-of-the-art approaches in accuracy and robustness, particularly in scenes with sparse structure, motion distortion, and dynamic interference, demonstrating its suitability for reliable real-world deployment. Full article
(This article belongs to the Special Issue LiDAR Technology for Autonomous Navigation and Mapping)
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