De-noise of Online Monitoring Basic Data Collected by Surveying Robots
Author(s) -
Wei Li,
Hongqi Xiong,
Houguang Sun,
Yachun Mao,
Hankang Zhang
Publication year - 2015
Publication title -
international journal of online and biomedical engineering (ijoe)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.184
H-Index - 8
ISSN - 2626-8493
DOI - 10.3991/ijoe.v11i2.4476
Subject(s) - computer science , noise (video) , azimuth , robot , landslide , warning system , data processing , remote sensing , computer vision , real time computing , artificial intelligence , data mining , engineering , image (mathematics) , geology , database , telecommunications , astronomy , physics , geotechnical engineering
—Three-dimensional online monitoring systems based on a surveying robot (TCA2003) are widely used in the slope monitoring of various open pits. A lot of noise is contained in basic monitoring data (azimuth, vertical angle, distance) because of various factors. Thus, the accuracy of basic monitoring data is greatly reduced, and this issue has become a limitation in landslide warning. In this paper, multi-cycle monitoring data from multiple open pits are used as data source and de-noised using different filtering methods. At the same time, filtering effect is evaluated using the image and accuracy of filtered basic data. Best filtering methods of different monitoring basic data are proposed, laying the foundation for automated processing of monitoring data based on a surveying robot.
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