Performance Evaluation of Lightweight LiDAR for UAV Applications
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Earth Sciences".
Deadline for manuscript submissions: closed (20 January 2022) | Viewed by 3723
Special Issue Editor
Interests: remote sensing data processing and interpretation; object and feature detection; learning systems; sensors for remote sensing; embedded systems
Special Issue Information
Dear Colleagues,
In the last decade, small and lightweight LiDAR systems have evolved all the way from research prototypes to easy-to-use cost-effective integrated commercial systems, supported by powerful point-cloud analysis software.
The automotive market drives the technology development for the sensors used on UAVs. Rotating- and oscillating-mirror systems are challenged by the development of micromirror-based of fully-solid-state units, which still do not reach the same performance.
Critical components of a LiDAR system are attitude and heading reference systems (AHRS) and GNSS units. Together with Kalman filtering and position correction software, they contribute substantially to determining the precision and accuracy of the system and its cost.
The performance of UAV-based LiDAR systems needs to be characterized from several complementary points of view. Basic descriptors concerning the integrated sensor include accuracy and precision of points geo-localization, beam width, pulse frequency, and number of returns. Post-processing software contributes significantly to accuracy and precision improvement. From an application point of view, the operator needs to evaluate the productivity and cost-effectiveness of the system, by taking into account workload related to field survey and back-office post-processing tasks, as well as equipment cost. Comparison with alternative survey methods, the most significant being photogrammetry, is necessary, taking into account different capabilities, e.g., for ground detection under vegetation cover, or wire detection.
Navigation and obstacle avoidance tasks can also profit from such sensing systems, both as purpose-specific sensors or using real-time data being gathered for surveys.
Contributions are welcome focusing on documented performance evaluation of UAV-borne LiDARs, with an emphasis on establishing rigorous performance metrics and methodologies, also specific to relevant case studies and applications. Availability of data is considered an added value.
Dr. Marco Balsi
Guest Editor
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Keywords
- LiDAR
- UAV
- accuracy
- precision
- performance metrics
- AHRS
- INS
- post-processing algorithms
- economic assessment
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