Path planning method of mobile lidar in plantation sample plot survey

Guan Tianshuo; Wang Chunbo; Wang Wei; Yan Fei; Fan Yongxiang

doi:10.12171/j.1000-1522.20220273

Journal of Beijing Forestry University > 2024 > 46(5): 154-162. > DOI: 10.12171/j.1000-1522.20220273

Guan Tianshuo, Wang Chunbo, Wang Wei, Yan Fei, Fan Yongxiang. Path planning method of mobile lidar in plantation sample plot survey[J]. Journal of Beijing Forestry University, 2024, 46(5): 154-162. DOI: 10.12171/j.1000-1522.20220273

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Path planning method of mobile lidar in plantation sample plot survey

1.
Precision Forestry Key Laboratory of Beijing, Beijing Forestry University, Beijing 100083, China
2.
Geophysical Survey Team of Hebei Province Coal Geological Bureau, Xingtai 054000, Hebei, China
3.
Tibet Agricultural and Animal Husbandry University, Nyingchi 860000, Xizang, China
4.
Jihua Laboratory, Foshan 528000, Guangdong, China

More Information

Received Date: July 06, 2022
Revised Date: December 05, 2023
Accepted Date: December 05, 2023
Available Online: December 07, 2023

Graphical Abstract

Abstract

Abstract

Objective
Forestry survey planning is important for the sustainable development of forestry. When using mobile lidar to do forest sample plot mapping and measurement, the accuracy of the global consistency map is closely related to the scanning trajectory. Therefore, the rational planning of sample plot observation path is of great significance.
Method
In this study, the forest sample plots were scanned by the handheld lidar with the use of simultaneous localization and mapping (SLAM). Based on the researches in the application of SLAM technology and the characteristics of forest sample land, three handheld mobile lidar scanning path plans in the sample plots were proposed. And the point cloud map was constructed through LeGO-LOAM algorithm. Thus, the three paths were compared and analyzed in terms of mapping effects, the accuracy of diameter of standing wood chest and the accuracy of its position (curve fitting data).
Result
The estimated diameter of the prototype stumpage chest diameter fitted by the progressive path 1 had a deviation of 2.18 cm, a relative deviation of 7.74%, and a root mean square error (RMSE) of 2.74 cm, and its accuracy was higher than the other two paths; path 1 fitted the rib diameter of the substation boundary standing wood and the internal standing wood better than path 2 and path 3; in terms of the standing wood position fitting accuracy, path 1 and path 3 had a better overall fitting effect, and the accuracy of path 1 was slightly better than path 3, with an x-axis estimated root mean square error (RMSE) of 0.077 m, a y-axis estimation root mean square error of 0.157 m, and a covariance difference of the maximum error direction of 0.124 m, all of which were smaller than the other two paths.
Conclusion
When collecting data through 32-line lidar, with the use of point cloud mapping of the forest-like land and the LeGO-LOAM algorithm, to achieve the fit measurement of the single wood factor, the overall mapping effect and the measurement accuracy of the aerial photogrammetry route like path plan (Path 1) are relatively better than the other paths. Based on this path plan, the mapping and measurement accuracy can be further improved by increasing the ground identification point and increasing the distance between the scanning path boundary and the sample point boundary. This can provide a reasonable path plan design for ground mobile lidar field data acquisition.
- forestry survey,
- LiDAR,
- SLAM technology,
- accuracy verification

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References (21)

References

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Path planning method of mobile lidar in plantation sample plot survey