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Outline
To address the impact of blurred environmental features caused by snow and ice coverage on LiDAR point clouds during real-time laser-based construction and Simultaneous Localization and Mapping (SLAM) in extremely cold environments, this paper employs the LIO-SAM algorithm framework. In the point cloud preprocessing stage, a Dynamic Radius Outlier Removal (DROR) filter is used to eliminate snow-induced noise while preserving the details of environmental features, thereby providing high-quality point cloud data for subsequent processing by the LIO-SAM algorithm. Validation results on the CADC dataset show that, compared to the filter built into LIO-SAM, the point cloud data collected under snowy conditions achieves a recall rate exceeding 91% while maintaining high accuracy. This approach effectively removes snow-induced noise while preserving more environmental features.
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| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
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