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For the problem of mapping failure in the high-speed tracking and figure-eight scenarios of the Formula Student Autonomous China (FSAC) due to the limited recognition and low accuracy of single-sensor cone detection, this paper proposes a cone mapping algorithm based on the loose coupling of LiDAR, industrial cameras, and a combined inertial navigation system. By projecting LiDAR data onto the camera coordinate system, the similarity between the target detection bounding boxes from the camera’s deep learning framework (YOLOv5) and the LiDAR cone bounding boxes is matched. The fused point cloud, containing RGB color information, is then transformed from the LiDAR coordinate system to the map coordinate system. The real-time vehicle pose calculated by the combined inertial navigation system is used to update the fused cone point cloud map. Real-vehicle comparative test results show that the algorithm achieves an average recall rate of 98.6% and an average precision of 99.1%, enabling the distinction between the inner and outer tracks of the cone map, thereby enhancing the vehicle’s perception, anticipation capabilities and path planning efficiency.
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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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