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Underwater Object Recognition Using Point-Features, Bayesian Estimation and Semantic Information
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DOI：10.3390/s21051807.png)
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摘要
En 中文
This paper proposes a 3D object recognition method for non-coloured point clouds using point features. The method is intended for application scenarios such as Inspection, Maintenance and Repair (IMR) of industrial sub-sea structures composed of pipes and connecting objects (such as valves, elbows and R-Tee connectors). The recognition algorithm uses a database of partial views of the objects, stored as point clouds, which is available a priori. The recognition pipeline has 5 stages: (1) Plane segmentation, (2) Pipe detection, (3) Semantic Object-segmentation and detection, (4) Feature based Object Recognition and (5) Bayesian estimation. To apply the Bayesian estimation, an object tracking method based on a new Interdistance Joint Compatibility Branch and Bound (IJCBB) algorithm is proposed. The paper studies the recognition performance depending on: (1) the point feature descriptor used, (2) the use (or not) of Bayesian estimation and (3) the inclusion of semantic information about the objects connections. The methods are tested using an experimental dataset containing laser scans and Autonomous Underwater Vehicle (AUV) navigation data. The best results are obtained using the Clustered Viewpoint Feature Histogram (CVFH) descriptor, achieving recognition rates of 51.2%, 68.6% and 90%, respectively, clearly showing the advantages of using the Bayesian estimation (18% increase) and the inclusion of semantic information (21% further increase).
Keyword:
3D object recognition
point clouds
global descriptors
semantic segmentation
semantic information
Bayesian probabilities
laser scanner
underwater environment
pipeline detection
inspection
maintenance and repair
AUV
autonomous manipulation
multi-object tracking
JCBB
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期刊
IF:
3.5
论文数:
7.2W
被引数:
20.9W
机构
引用论文
Automatic pipe and elbow recognition from three-dimensional point cloud model of industrial plant piping system using convolutional neural network-based primitive classification基于卷积神经网络的工业厂房管道系统三维点云模型的管道和弯头自动识别
PREAI
分享收藏
3D Object Recognition and Pose Estimation From Point Cloud Using Stably Observed Point Pair Feature使用稳定观察的点对特征从点云进行3D物体识别和姿态估计.png)
IEEE ACCESSOAAI
IEEE ACCESSIF3.6
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