3D Point Cloud Registration Algorithm Based on Feature Matching

Jian, 刘剑 Liu; Di, 白迪 Bai

doi:10.3788/aos201838.1215005

Cited by 8 publications

(1 citation statement)

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“…The standard effect is not very good. (3) Feature-based matching methods, [7][8][9][10][11][12][13][14][15] aimed at feature extraction and description, and improve the accuracy of the registration algorithm through different feature extraction algorithms and descriptions, but the parameters of some algorithms should not be adjusted, and different point clouds need to adjust different parameters; the above feature methods are not robust when the point cloud is severely occluded. In order to solve this problem, Drost et al [16] proposed a point pair feature algorithm (PPF), which combines the global and local advantages and can be used to quickly complete the point cloud feature description, pose voting, and solve the rough registration process of the rotation and translation matrices.…”

Section: Introductionmentioning

confidence: 99%

A Fast Point Cloud Recognition Algorithm Based on Keypoint Pair Feature

Zhang

Shen

Gao

et al. 2022

Sensors

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At present, PPF-based point cloud recognition algorithms can perform better matching than competitors and be verified in the case of severe occlusion and stacking. However, including certain superfluous feature point pairs in the global model description would significantly lower the algorithm’s efficiency. As a result, this paper delves into the Point Pair Feature (PPF) algorithm and proposes a 6D pose estimation method based on Keypoint Pair Feature (K-PPF) voting. The K-PPF algorithm is based on the PPF algorithm and proposes an improved algorithm for the sampling point part. The sample points are retrieved using a combination of curvature-adaptive and grid ISS, and the angle-adaptive judgment is performed on the sampling points to extract the keypoints, therefore improving the point pair feature difference and matching accuracy. To verify the effectiveness of the method, we analyze the experimental results in scenes with different occlusion and complexity levels under the evaluation metrics of ADD-S, Recall, Precision, and Overlap rate. The results show that the algorithm in this paper reduces redundant point pairs and improves recognition efficiency and robustness compared with PPF. Compared with FPFH, CSHOT, SHOT and SI algorithms, this paper improves the recall rate by more than 12.5%.

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