Drivable Dirt Road Region Identification Using Image and Point Cloud Semantic Segmentation Fusion

Park, Jihun; Yoo, Hyunsung; Wang, Yooseung

doi:10.1109/tits.2021.3121710

Cited by 18 publications

(2 citation statements)

References 49 publications

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“…Xu Ming [3] analyzed the grayscale features of unstructured roads and used HSV space and the two-dimensional maximum inter-class variance algorithm to perform image segmentation and identify the drivable areas of the road. Park [4] integrates semantic segmentation algorithms and point cloud segmentation algorithms. The alphashape algorithm is used to convert the point-by-point drivable area recognition results into regional information.…”

Section: Introductionmentioning

confidence: 99%

Segmentation method of drivable areas of mine roads based on rut texture features

Yuan,

Huang,

Xiong

2024

Third International Conference on Advanced Manufacturing Technology and Electronic Information (AMTEI 2023)

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The mine road is unstructured, the line shape is changeable, and the color of the road surface is similar to that of the mountains on both sides. In order to make up for the deficiency of the segmentation effect of the road detection model based on shape and color, a driving area segmentation method based on rut texture features is proposed. Initially, the collected images are preprocessed in the region of interest. Secondly, the gray level co-occurrence matrix(GLCM) is used to obtain the characteristic parameters of the rutting area, and four texture feature indexes are used as the input feature vectors of the genetic algorithm(GA) to obtain the optimal segmentation threshold. Finally, by filling the hole noise in the segmented image, a complete drivable area is obtained. The comparison test shows that the proposed segmentation method based on rut texture features can effectively overcome the problem that the road is similar to the background color and is difficult to segment. Compared with other similar segmentation methods, the segmentation effect of the road has better accuracy and robustness.

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Section: Introductionmentioning

confidence: 99%

Segmentation method of drivable areas of mine roads based on rut texture features

Yuan,

Huang,

Xiong

2024

Third International Conference on Advanced Manufacturing Technology and Electronic Information (AMTEI 2023)

View full text Add to dashboard Cite

show abstract

“…Occupancy grid [4][7] [8] is the basic form of grid map. Input from LiDAR, camera and other sensors are used to calculate the occupancy probability of each grid point, achieving overall representation of the accessibility of the whole environment [9][10] [11]. For an indoor robot, driving task could be achieved by path searching in the unoccupied free area.…”

Section: Introductionmentioning

confidence: 99%