Point cloud registration method based on 3DMatch network and improved ISS algorithm

Zhang, Zhongda; Tang, Jialin; Niu, Lihong; Su, Binghua; Feng, Yi; Sheng, Yicheng; Lin, Shounan; Cheng, Xipeng

doi:10.1109/iscer55570.2022.00034

Cited by 2 publications

(1 citation statement)

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“…Guo [6] proposed a practical method for coarse point cloud detection based on image feature points to reduce the number of iterations for fine-tuning. Zhang [7] utilizes an enhanced ISS key point extraction algorithm to address the issue of the point cloud's initial high position, and the low matching rate caused by the classical 3D Match's random sampling strategy that results in inconspicuous features. Toldo [8] proposes the use of a generalized Pluck analysis, which takes into account the simultaneous recording of all the point cloud data included in the ICP algorithm.…”

Section: Introductionmentioning

confidence: 99%

Multi-View consistency-based point cloud registration method with low overlap rate

Cui,

Liao,

Lin

et al. 2024

J. Phys.: Conf. Ser.

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Accurate and efficient workpiece measurement is crucial for workpiece processing and quality monitoring. Non-contact optical measurement methods have gained more attention due to their simplicity, efficiency, and flexibility compared to complicated and inefficient contact measurement methods. Multi-view registration of measurement data is a key issue in workpiece measurement, as it relies on the system’s geometric accuracy and motion stability, presenting challenges such as the insufficient overlap of multi-viewpoint cloud data and cumulative error. To address these challenges, this paper proposes a multi-view planning and registration algorithm with a low overlap rate. The multi-view planning algorithm employs a greedy method to plan the scanning viewpoints of the workpiece to obtain complete point cloud data efficiently. The multi-view registration algorithm extracts features using a multi-scale geometric feature extraction network, matches the features based on the Hungarian algorithm, builds a graph, and optimizes workpiece positions based on the G2O algorithm for multi-view registration, effectively reducing cumulative error. Measurement experiments on blade workpieces confirm the feasibility of the proposed algorithms.

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Section: Introductionmentioning

confidence: 99%