马氏距离
离群值
点云
计算机科学
人工智能
兰萨克
模式识别(心理学)
预处理器
特征(语言学)
异常检测
可视化
计算机视觉
数据挖掘
图像(数学)
语言学
哲学
作者
Chengzhi Qu,Yan Zhang,Kun Huang,Shuang Wang,Yang Yang
出处
期刊:IEEE robotics and automation letters
日期:2023-01-01
卷期号:8 (1): 17-24
标识
DOI:10.1109/lra.2022.3221315
摘要
Point clouds have been regarded as a representative format for 3D visualization of real-world objects or scenes. However, point clouds acquired from depth cameras or laser scanning devices commonly contain outliers. Outlier removal performance will directly affect the downstream applications. Existing methods mainly perform outlier removal directly on the raw data, which are designed without finding a proper balance between outlier removal precision and detail feature retention. In this letter, we propose a novel outlier removal method called CIMD that can obtain outlier-free data by conducting the improved Mahalanobis distance and point clouds completion. A layered statistical outlier removal approach is introduced as preprocessing strategy to obtain feature points and incomplete point clouds. We filter feature points to fill the incomplete point clouds according to the improved Mahalanobis distances. The theoretical analysis proves that the improved Mahalanobis distance can magnify the difference between outliers and ground truth compared with the original way. Using publicly available dataset PointCleanNet and real-world scanned data, the experimental results show that the proposed method has superior performance compared with state-of-the-art methods.
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