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Journal Article Reflective Noise Filtering of Large-Scale Point Cloud Using Multi-Position LiDAR Sensing Data
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Authors
Rui Gao, Jisun Park, Xiaohang Hu, Seungjun Yang, Kyungeun Cho
Issue Date
2021-08
Citation
Remote Sensing, v.13, no.16, pp.1-22
ISSN
2072-4292
Publisher
MDPI
Language
English
Type
Journal Article
DOI
https://dx.doi.org/10.3390/rs13163058
Abstract
Signals, such as point clouds captured by light detection and ranging sensors, are often affected by highly reflective objects, including specular opaque and transparent materials, such as glass, mirrors, and polished metal, which produce reflection artifacts, thereby degrading the performance of associated computer vision techniques. In traditional noise filtering methods for point clouds, noise is detected by considering the distribution of the neighboring points. However, noise generated by reflected areas is quite dense and cannot be removed by considering the point distribution. Therefore, this paper proposes a noise removal method to detect dense noise points caused by reflected objects using multi-position sensing data comparison. The proposed method is divided into three steps. First, the point cloud data are converted to range images of depth and reflective intensity. Second, the reflected area is detected using a sliding window on two converted range images. Finally, noise is filtered by comparing it with the neighbor sensor data between the detected reflected areas. Experiment results demonstrate that, unlike conventional methods, the proposed method can better filter dense and large-scale noise caused by reflective objects. In future work, we will attempt to add the RGB image to improve the accuracy of noise detection.
KSP Keywords
Computer Vision(CV), Conventional methods, Data comparison, Experiment results, Filtering method, Large-scale point cloud, Light detection and Ranging(LiDAR), Neighboring points, Noise Removal, Noise filtering, Point Cloud Data
This work is distributed under the term of Creative Commons License (CCL)
(CC BY)
CC BY