TY - GEN
T1 - Plane Detection Based on an Improved RANSAC Algorithm
AU - Li, Peng
AU - Wang, Mao
AU - Fu, Jinyu
AU - Wang, Yankun
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - When obtaining point cloud data of the measured object through 3D scanning, it is inevitable to encounter noise and outliers, which seriously affect the accuracy of estimating point cloud plane parameters and fitting planes. The Random Sample Consensus (RANSAC) algorithm can effectively estimate point cloud plane parameters and fit planes with certain robustness, but it has redundancy as it needs to distinguish inliers from outliers in each iteration, which has a certain impact on running efficiency. This article proposes an improved RANSAC algorithm based on Principal Component Analysis (PCA) method, combined with setting certain criteria to eliminate gross errors and outliers in point cloud data, in order to obtain ideal plane fitting parameters. Experiments show that compared with some traditional algorithms, this method can adapt well to the presence of gross errors and outliers in point cloud data, obtain better estimates of plane parameters, and is a robust plane fitting algorithm.
AB - When obtaining point cloud data of the measured object through 3D scanning, it is inevitable to encounter noise and outliers, which seriously affect the accuracy of estimating point cloud plane parameters and fitting planes. The Random Sample Consensus (RANSAC) algorithm can effectively estimate point cloud plane parameters and fit planes with certain robustness, but it has redundancy as it needs to distinguish inliers from outliers in each iteration, which has a certain impact on running efficiency. This article proposes an improved RANSAC algorithm based on Principal Component Analysis (PCA) method, combined with setting certain criteria to eliminate gross errors and outliers in point cloud data, in order to obtain ideal plane fitting parameters. Experiments show that compared with some traditional algorithms, this method can adapt well to the presence of gross errors and outliers in point cloud data, obtain better estimates of plane parameters, and is a robust plane fitting algorithm.
KW - PCA
KW - RANSAC
KW - plane detection
KW - point cloud
UR - https://www.scopus.com/pages/publications/85169290451
U2 - 10.1109/CCAI57533.2023.10201261
DO - 10.1109/CCAI57533.2023.10201261
M3 - 会议稿件
AN - SCOPUS:85169290451
T3 - 2023 IEEE 3rd International Conference on Computer Communication and Artificial Intelligence, CCAI 2023
SP - 211
EP - 215
BT - 2023 IEEE 3rd International Conference on Computer Communication and Artificial Intelligence, CCAI 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 3rd IEEE International Conference on Computer Communication and Artificial Intelligence, CCAI 2023
Y2 - 26 May 2023 through 28 May 2023
ER -