Research on 3D Point Cloud De-Distortion Algorithm and Its Application on Euclidean Clustering

Long, Wen; He, Lei; Gao, Zhenhai

doi:10.1109/access.2019.2926424

Cited by 14 publications

(8 citation statements)

References 19 publications

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“…Fast Euclidean clustering (FEC) employs a pointwise scheme to enhance the performance of Euclidean clustering [19]. Wen optimized the structure of the Euclidean clustering algorithm and improved its operational efficiency [20]. In [21], researchers introduced a probabilistic framework that integrates both the Euclidean spatial information and the temporal information derived from consecutive frames.…”

Section: Point Cloud Cluster Methodsmentioning

confidence: 99%

A Real-Time Vessel Detection and Tracking System Based on LiDAR

Qi,

Huang,

Zhang

et al. 2023

Sensors

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Vessel detection and tracking is of utmost importance to river traffic. Efficient detection and tracking technology offer an effective solution to address challenges related to river traffic safety and congestion. Traditional image-based object detection and tracking algorithms encounter issues such as target ID switching, difficulties in feature extraction, reduced robustness due to occlusion, target overlap, and changes in brightness and contrast. To detect and track vessels more accurately, a vessel detection and tracking algorithm based on the LiDAR point cloud was proposed. For vessel detection, statistical filtering algorithms were integrated into the Euclidean clustering algorithm to mitigate the effect of ripples on vessel detection. Our detection accuracy of vessels improved by 3.3% to 8.3% compared to three conventional algorithms. For vessel tracking, L-shape fitting of detected vessels can improve the efficiency of tracking, and a simple and efficient tracking algorithm is presented. By comparing three traditional tracking algorithms, an improvement in multiple object tracking accuracy (MOTA) and a reduction in ID switch times and number of missed detections were achieved. The results demonstrate that LiDAR point cloud-based vessel detection can significantly enhance the accuracy of vessel detection and tracking.

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Section: Point Cloud Cluster Methodsmentioning

confidence: 99%

A Real-Time Vessel Detection and Tracking System Based on LiDAR

Qi,

Huang,

Zhang

et al. 2023

Sensors

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“…Yabroudi [22] divided the space into different rectangles through the FOV angle of view, adopted different clustering radii inside each rectangle and then used the DBSCAN algorithm to complete the clustering. Wen [23] deleted the point cloud with a height within a certain range as the ground point cloud and then proposed an improved European clustering algorithm, which calculated and calibrated the optimal clustering threshold under different distances. The selection of cluster radius in the density-based clustering algorithm is the main factor affecting the quality of point cloud clustering.…”

Section: Related Researchmentioning

confidence: 99%

Robust LiDAR-Based Vehicle Detection for On-Road Autonomous Driving

Jin

Yang

et al. 2023

Remote Sensing

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The stable detection and tracking of high-speed vehicles on the road by using LiDAR can input accurate information for the decision-making module and improve the driving safety of smart cars. This paper proposed a novel LiDAR-based robust vehicle detection method including three parts: point cloud clustering, bounding box fitting and point cloud recognition. Firstly, aiming at the problem of clustering quality degradation caused by the uneven distribution of LiDAR point clouds and the difference in clustering radius between point cloud clusters in traditional DBSCAN (TDBSCAN) obstacle clustering algorithms, an improved DBSCAN algorithm based on distance-adaptive clustering radius (ADBSCAN) is designed, and a point cloud KD-Tree data structure is constructed to speed up the traversal of the algorithm; meanwhile, the OPTICS algorithm is introduced to enhance the performance of the proposed algorithm. Then, by adopting different fitting strategies for vehicle contour points in various states, the adaptability of the bounding box fitting algorithm is improved; Moreover, in view of the shortcomings of the poor robustness of the L-shape algorithm, the principal component analysis method (PCA) is introduced to obtain stable bounding box fitting results. Finally, considering the time-consuming and low-accuracy training of traditional machine learning algorithms, advanced PointNet in deep learning technique is built to send the point cloud within the bounding box of a high-confidence vehicle into PointNet to complete vehicle recognition. Experiments based on our autonomous driving perception platform and the KITTI dataset prove that the proposed method can stably complete vehicle target recognition and achieve a good balance between time-consuming and accuracy.

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“…At present, the development of non-contact laser scanning technology has helped improve inspection objectivity and simulate a real matched state based on PCD. This research is developing rapidly and has made significant advancements in many areas, there are many scholars and engineers apply it to all walks of life [25,26], Li provided new technology for a complex curved surface quality inspection method based on PCD [27]. An experimental analysis of complex parts proved the efficiency of the algorithm.…”

Section: Parts Inspectionmentioning

confidence: 99%

“…The objective function is shown in Equation (23). The students are randomly generated as shown in Equation (26). Then go to Step 3.…”

Section: Coarse Registration Of Pcdmentioning

confidence: 99%

An Advanced Vehicle Body Part Inspection Scheme Based on Scattered Point Cloud Data

Yang

2020

Applied Sciences

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To further improve the efficiency and accuracy of the vehicle part inspection process, this paper designs an accurate and efficient vehicle body part inspection framework based on scattered point cloud data (PCD). Firstly, a hybrid filtering algorithm for point cloud denoising is designed to solve the problem of multiple noise points in the original point cloud measurement data. Secondly, a point cloud simplification algorithm based on Fuzzy C-Means (FCM) is designed to solve the problems of a large amount of data and many redundant points in the PCD. Thirdly, a point cloud fine registration algorithm based on the Teaching-Learning-based Optimization (TLBO) algorithm is designed to solve the problem where the initial point cloud measurement data cannot be located properly. Finally, the deviation distance between the PCD and Computer-Aided-Design (CAD) model is calculated by the K-Nearest Neighbor (KNN) algorithm to inspect and analyze the point cloud after preprocessing. On the basis of the design algorithm, four groups that contain measurement data for eight vehicle body parts are analyzed and the results prove the effectiveness of the algorithm, which is very suitable for the inspection process of vehicle body parts.

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Research on 3D Point Cloud De-Distortion Algorithm and Its Application on Euclidean Clustering

Cited by 14 publications

References 19 publications

A Real-Time Vessel Detection and Tracking System Based on LiDAR

A Real-Time Vessel Detection and Tracking System Based on LiDAR

Robust LiDAR-Based Vehicle Detection for On-Road Autonomous Driving

An Advanced Vehicle Body Part Inspection Scheme Based on Scattered Point Cloud Data

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