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Special Issue "LiDAR for Autonomous Vehicles"

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Remote Sensors, Control, and Telemetry".

Deadline for manuscript submissions: 30 May 2020.

Special Issue Editors

Prof. Michele Grassi
Website
Guest Editor
University of Naples Federico II | UNINA · Department of Industrial Engineering
Interests: GNC of space systems; spacecraft relative navigation; pose determination; electro-optical sensors; LIDAR; star tracker; GNSS
Prof. Dr. Roberto Opromolla
Website
Guest Editor
Università degli Studi di Napoli Federico II, Naples, Italy
Interests: spacecraft relative navigation; pose determination; electro-optical sensors; LIDAR; star tracker; unmanned aerial vehicles; autonomous navigation; sense and avoid
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

LiDAR systems represent a key technology to enhance the capabilities of future autonomous vehicles in terms of navigation and situational awareness. Indeed, significant advancements are needed by terrestrial, marine, aerial, and space vehicles to meet the levels of safety and performance required within present and future application scenarios.
Self-driving cars, boats, and drones are expected to rely on LIDAR to enable autonomous and safe navigation, especially in GNSS-denied (e.g., indoor, underground, underwater) and GNSS-challenging (e.g., natural and urban canyon) areas. This task requires the development of accurate and robust localization algorithms, e.g., based on the concepts of odometry, and simultaneous localization and mapping. These approaches will exploit either LIDAR alone, or the integration of multiple sensors (e.g., inertial, cameras, radars), taking advantage of sensor fusion or cross-sensor cueing strategies. Regarding situational awareness, LIDAR can be used on board autonomous vehicle for detection and avoidance of static and moving obstacles, and for monitoring/inspecting infrastructures like roads, pipelines, and powerlines. These systems can also assist precision agriculture applications (especially on board autonomous aerial vehicles) by mapping water flow and catchments, monitoring erosion and soil loss, providing 3D models of crops, forestry, and vegetation.
LIDARs play a key role also in the space domain. On one side, mission scenarios such as on-orbit servicing and active debris removal, which require an autonomous spacecraft (chaser) to safely perform maneuvers (such as rendezvous and docking) in close proximity with respect to uncooperative space targets, can rely on LIDAR to provide accurate estimates of the target-chaser relative state. This task requires advanced pose determination algorithms as well as robust relative navigation architectures. On the other side, LIDAR-based navigation systems are important for future deep space exploration scenarios, e.g., during precise descent and landing operations on planets, comets or asteroids. In this framework, advanced algorithmic solutions shall be developed for both navigation, and hazard detection and avoidance (landing site selection).

Hence, this Special Issue welcomes original research contributions and state-of-the-art reviews, from academia and industry, regarding the use of LIDAR technologies on board autonomous vehicles. In addition to state-of-the-art LIDARs (e.g., scanning and flash LIDAR), innovative solutions based on the use of solid-state technologies are also welcome. The Special Issue topics include but are not limited to:

  • Autonomous navigation solutions (e.g., odometry, simultaneous localization, and mapping);
  • Autonomous detect and avoid (e.g., for static or moving obstacles);
  • Infrastructure (e.g., powerlines, pipelines, roads) inspection and monitoring;
  • 3D mapping;
  • Precision agriculture (e.g., canopy height estimation, erosion, and soil loss monitoring);
  • Spacecraft relative navigation and pose determination;
  • Autonomous navigation and situational awareness in deep space exploration scenarios (e.g., hazard detection and precise landing).

Prof. Michele Grassi
Prof. Roberto Opromolla
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 papers will be 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. Sensors 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 2000 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

  • Scanning, flash and solid-state LIDARs for autonomous vehicles
  • Unmanned ground vehicles (UGV)
  • Unmanned surface vehicles (USV)
  • Unmanned aerial vehicles (UAV)
  • Localization by odometry
  • Localization by simultaneous localization and mapping
  • Obstacle detection and avoidance
  • Infrastructure inspection and monitoring
  • 3D mapping
  • Precision agriculture
  • Spacecraft relative navigation and pose determination
  • Planetary landing
  • Hazard detection

Published Papers (2 papers)

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Research

Open AccessArticle
Extrinsic Calibration of Multiple Two-Dimensional Laser Rangefinders Based on a Trihedron
Sensors 2020, 20(7), 1837; https://doi.org/10.3390/s20071837 - 26 Mar 2020
Abstract
Multiple two-dimensional laser rangefinders (LRFs) are applied in many applications like mobile robotics, autonomous vehicles, and three-dimensional reconstruction. The extrinsic calibration between LRFs is the first step to perform data fusion and practical application. In this paper, we proposed a simple method to [...] Read more.
Multiple two-dimensional laser rangefinders (LRFs) are applied in many applications like mobile robotics, autonomous vehicles, and three-dimensional reconstruction. The extrinsic calibration between LRFs is the first step to perform data fusion and practical application. In this paper, we proposed a simple method to calibrate LRFs based on a corner composed of three mutually perpendicular planes. In contrast to other methods that require a special pattern or assistance from other sensors, the trihedron corner needed in this method is common in daily environments. In practice, we can adjust the position of the LRFs to observe the corner until the laser scanning plane intersects with three planes of the corner. Then, we formed a Perspective-Three-Point problem to solve the position and orientation of each LRF at the common corner coordinate system. The method was validated with synthetic and real experiments, showing better performance than existing methods. Full article
(This article belongs to the Special Issue LiDAR for Autonomous Vehicles)
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Open AccessArticle
Optimized LOAM Using Ground Plane Constraints and SegMatch-Based Loop Detection
Sensors 2019, 19(24), 5419; https://doi.org/10.3390/s19245419 - 09 Dec 2019
Cited by 1
Abstract
Reducing the cumulative error in the process of simultaneous localization and mapping (SLAM) has always been a hot issue. In this paper, in order to improve the localization and mapping accuracy of ground vehicles, we proposed a novel optimized lidar odometry and mapping [...] Read more.
Reducing the cumulative error in the process of simultaneous localization and mapping (SLAM) has always been a hot issue. In this paper, in order to improve the localization and mapping accuracy of ground vehicles, we proposed a novel optimized lidar odometry and mapping method using ground plane constraints and SegMatch-based loop detection. We only used the lidar point cloud to estimate the pose between consecutive frames, without any other sensors, such as Global Positioning System (GPS) and Inertial Measurement Unit (IMU). Firstly, the ground plane constraints were used to reduce matching errors. Then, based on more accurate lidar odometry obtained from lidar odometry and mapping (LOAM), SegMatch completed segmentation matching and loop detection to optimize the global pose. The neighborhood search was also used to accomplish the loop detection task in case of failure. Finally, the proposed method was evaluated and compared with the existing 3D lidar SLAM methods. Experiment results showed that the proposed method could realize low drift localization and dense 3D point cloud map construction. Full article
(This article belongs to the Special Issue LiDAR for Autonomous Vehicles)
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