A Novel Method for Obstacle Detection in Front of Vehicles Based on the Local Spatial Features of Point Cloud

Ci, Wenyan; Xu, Tao; Lin, Runze; Lu, Shan; Wu, Xialai; Xuan, Jiayin

doi:10.3390/rs15041044

Cited by 3 publications

(2 citation statements)

References 29 publications

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“…For evaluating denoising results, this paper selected the first type of error (false positive rate) and the second type of error (false negative rate) in point cloud filtering as evaluation metrics (Ci et al 2023), as per equation ( 19)…”

Section: Evaluation Of Resultsmentioning

confidence: 99%

Denoising algorithm for inclined tunnel point cloud data based on irregular contour features

Song,

Jin,

et al. 2024

Meas. Sci. Technol.

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In response to the problem where current traditional denoising algorithms cannot effectively remove noise from inclined tunnel point cloud data with irregular contours. This paper proposes a denoising algorithm for inclined tunnel point cloud data based on irregular contour features. The algorithm combines the DBSCAN clustering algorithm with polynomial curve fitting to obtain sequential point cloud slices along the perpendicular direction to the centerline of the inclined tunnel. By identifying and extracting irregular contour feature points from these slices, it achieves the extraction of irregular wall shapes inside the tunnel. Based on these irregular wall shape features, noise points are effectively removed using distance iteration calculations. Experimental results demonstrate that the proposed algorithm can effectively handle the irregular shapes and elevation variations in inclined tunnel point cloud data and achieve good denoising performance for various types of noise within the tunnel. This algorithm lays a solid foundation for subsequent three-dimensional modeling of tunnels with high precision.

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Section: Evaluation Of Resultsmentioning

confidence: 99%

Denoising algorithm for inclined tunnel point cloud data based on irregular contour features

Song,

Jin,

et al. 2024

Meas. Sci. Technol.

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“…However, achieving a balance between timeliness and accuracy has been proven to be difficult for these algorithms. Currently, the application of lidar in obstacle detection is still focused on detecting large obstacles such as cars and pedestrians, and a small number of research studies have used it for detecting small obstacles [19]. Vitor et al [12] used a 2D laser scanner to design an obstacle detection device for railway intersections, successfully completing the recognition of obstacles with a height of 30 cm.…”

Section: Introductionmentioning

confidence: 99%

Development of a High-Precision Lidar System and Improvement of Key Steps for Railway Obstacle Detection Algorithm

Nan,

Zhu,

Zhang

et al. 2024

Remote Sensing

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In response to the growing demand for railway obstacle monitoring, lidar technology has emerged as an up-and-coming solution. In this study, we developed a mechanical 3D lidar system and meticulously calibrated the point cloud transformation to monitor specific areas precisely. Based on this foundation, we have devised a novel set of algorithms for obstacle detection within point clouds. These algorithms encompass three key steps: (a) the segmentation of ground point clouds and extraction of track point clouds using our RS-Lo-RANSAC (region select Lo-RANSAC) algorithm; (b) the registration of the BP (background point cloud) and FP (foreground point cloud) via an improved Robust ICP algorithm; and (c) obstacle recognition based on the VFOR (voxel-based feature obstacle recognition) algorithm from the fused point clouds. This set of algorithms has demonstrated robustness and operational efficiency in our experiments on a dataset obtained from an experimental field. Notably, it enables monitoring obstacles with dimensions of 15 cm × 15 cm × 15 cm. Overall, our study showcases the immense potential of lidar technology in railway obstacle monitoring, presenting a promising solution to enhance safety in this field.

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An Obstacle Detection Method Based on Longitudinal Active Vision

Shi,

Ni,

Kong

et al. 2024

Sensors

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The types of obstacles encountered in the road environment are complex and diverse, and accurate and reliable detection of obstacles is the key to improving traffic safety. Traditional obstacle detection methods are limited by the type of samples and therefore cannot detect others comprehensively. Therefore, this paper proposes an obstacle detection method based on longitudinal active vision. The obstacles are recognized according to the height difference characteristics between the obstacle imaging points and the ground points in the image, and the obstacle detection in the target area is realized without accurately distinguishing the obstacle categories, which reduces the spatial and temporal complexity of the road environment perception. The method of this paper is compared and analyzed with the obstacle detection methods based on VIDAR (vision-IMU based detection and range method), VIDAR + MSER, and YOLOv8s. The experimental results show that the method in this paper has high detection accuracy and verifies the feasibility of obstacle detection in road environments where unknown obstacles exist.

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A Novel Method for Obstacle Detection in Front of Vehicles Based on the Local Spatial Features of Point Cloud

Cited by 3 publications

References 29 publications

Denoising algorithm for inclined tunnel point cloud data based on irregular contour features

Denoising algorithm for inclined tunnel point cloud data based on irregular contour features

Development of a High-Precision Lidar System and Improvement of Key Steps for Railway Obstacle Detection Algorithm

An Obstacle Detection Method Based on Longitudinal Active Vision

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