Westerman, Arien (2024) A sensitivity evaluation of various LiDARs in dusty agricultural conditions. [USQ Project]
Abstract
This study presents a sensitivity evaluation of multiple LiDAR sensors in dusty agricultural environments, assessing the efficacy of both rotary and solid-state LiDARs under various particulate conditions. LiDAR technology, commonly used in agricultural robotics, enables three-dimensional mapping through active pulse-based ranging, but dust interference remains a critical limitation for accurate data capture. The project explores LiDAR’s advantages over other ranging methods like sonar and radar, emphasizing light's minimal attenuation in air and LiDAR’s high-resolution capability.
The project focussed on the distinct role of LiDAR in agricultural applications, such as crop monitoring and yield estimation, and further on existing limitations in environments with airborne particulates. By agitating soil to simulate a representative range of dust types encountered during agricultural activities, preliminary experiments were conducted in a field environment. Solid-State LiDAR was found less sensitive to dust interference than solid-state LiDAR in further testing, although environmental challenges, particularly rain, constrained data quantity, while limitations in software access limited the quality of the analysis. Solid-state LiDAR demonstrated improved visibility, potentially indicating a lower susceptibility to particle occlusion but also reflected dust persistence over time.
This project discusses the experimental methods, including an evaluation of data alignment techniques between LiDAR sensors and video footage. It also considers potential adjustments to experimental protocols, such as controlled indoor conditions or the use of a leaf blower or other equipment to standardize dust dispersion. Findings suggest solid-state LiDAR's potential for more detailed particulate measurement, though further testing with refined dust release mechanisms is warranted. Future work includes the development of automated data comparison software, enhancing LiDAR data reliability across diverse agricultural contexts.
|
Statistics for this ePrint Item |
| Item Type: | USQ Project |
|---|---|
| Item Status: | Live Archive |
| Faculty/School / Institute/Centre: | Current – Faculty of Health, Engineering and Sciences - School of Engineering (1 Jan 2022 -) |
| Supervisors: | Long, Derek |
| Qualification: | Bachelor of Engineering (Honours) (Mechatronics) |
| Date Deposited: | 18 Mar 2026 23:11 |
| Last Modified: | 18 Mar 2026 23:11 |
| Uncontrolled Keywords: | LiDAR sensors; agricultural environments |
| URI: | https://sear.unisq.edu.au/id/eprint/53175 |
Actions (login required)
 |
Archive Repository Staff Only |
