Laser & Optoelectronics Progress, Volume. 61, Issue 22, 2237003(2024)
Road Information Extraction Method Based on Hypervoxel Segmentation
Fig. 4. Self-collecting data experiment scene. (a) Experimental scene; (b) experimental platform details
Fig. 5. Rough extraction of ground points in IQTM point cloud dataset. (a) Primary point cloud; (b) point cloud after filtering non-ground point
Fig. 6. Registration results of road edge. (a) Target point cloud; (b) registration result by VCCS; (c) registration result by VCCS-kNN; (d) registration result by proposed method
Fig. 7. Extraction results of IQTM dataset. (a) Extraction result of boundary point; (b) extraction result of road edge
Fig. 8. Self-collected dataset. (a) Self-collected data; (b) hypervoxel segmentation result; (c) extraction result of boundary point; (d) extraction result of road edge
Fig. 9. Extraction results of lane line. (a) IQTM data driving area; (b) extraction result of IQTM data by global threshold method; (c) extraction result of IQTM data by proposed method; (d) self-collected data driving area; (e) extraction result of self-collected data by global threshold method; (f) extraction result of self-collected data by proposed method
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Zhe Su, Li Yang, Zai Luo, Wensong Jiang, Hongmei Fang. Road Information Extraction Method Based on Hypervoxel Segmentation[J]. Laser & Optoelectronics Progress, 2024, 61(22): 2237003
Category: Digital Image Processing
Received: Dec. 22, 2023
Accepted: Mar. 25, 2024
Published Online: Nov. 15, 2024
The Author Email: Zai Luo (luozai@cjlu.edu.cn)
CSTR:32186.14.LOP232716