Entropy-Based Registration of Point Clouds Using Terrestrial Laser Scanning and Smartphone GPS
Automatic registration of terrestrial laser scanning point clouds is a crucial but unresolved topic that is of great interest in many domains. This study combines terrestrial laser scanner with a smartphone for the coarse registration of leveled point clouds with small roll and pitch angles and heig...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2017-01-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | http://www.mdpi.com/1424-8220/17/1/197 |
| _version_ | 1798040210234146816 |
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| author | Maolin Chen Siying Wang Mingwei Wang Youchuan Wan Peipei He |
| author_facet | Maolin Chen Siying Wang Mingwei Wang Youchuan Wan Peipei He |
| author_sort | Maolin Chen |
| collection | DOAJ |
| description | Automatic registration of terrestrial laser scanning point clouds is a crucial but unresolved topic that is of great interest in many domains. This study combines terrestrial laser scanner with a smartphone for the coarse registration of leveled point clouds with small roll and pitch angles and height differences, which is a novel sensor combination mode for terrestrial laser scanning. The approximate distance between two neighboring scan positions is firstly calculated with smartphone GPS coordinates. Then, 2D distribution entropy is used to measure the distribution coherence between the two scans and search for the optimal initial transformation parameters. To this end, we propose a method called Iterative Minimum Entropy (IME) to correct initial transformation parameters based on two criteria: the difference between the average and minimum entropy and the deviation from the minimum entropy to the expected entropy. Finally, the presented method is evaluated using two data sets that contain tens of millions of points from panoramic and non-panoramic, vegetation-dominated and building-dominated cases and can achieve high accuracy and efficiency. |
| first_indexed | 2024-04-11T22:04:18Z |
| format | Article |
| id | doaj.art-90f8423c1d4349e58e7e9a4bfc9acc75 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T22:04:18Z |
| publishDate | 2017-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-90f8423c1d4349e58e7e9a4bfc9acc752022-12-22T04:00:46ZengMDPI AGSensors1424-82202017-01-0117119710.3390/s17010197s17010197Entropy-Based Registration of Point Clouds Using Terrestrial Laser Scanning and Smartphone GPSMaolin Chen0Siying Wang1Mingwei Wang2Youchuan Wan3Peipei He4School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, ChinaJiangsu Hi-Target Marine Technology Co., Ltd., Nanjin 210032, ChinaSchool of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, ChinaSchool of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, ChinaSchool of Resources and Environment, North China University of Water Resources and Electric Power, Zhengzhou 450046, ChinaAutomatic registration of terrestrial laser scanning point clouds is a crucial but unresolved topic that is of great interest in many domains. This study combines terrestrial laser scanner with a smartphone for the coarse registration of leveled point clouds with small roll and pitch angles and height differences, which is a novel sensor combination mode for terrestrial laser scanning. The approximate distance between two neighboring scan positions is firstly calculated with smartphone GPS coordinates. Then, 2D distribution entropy is used to measure the distribution coherence between the two scans and search for the optimal initial transformation parameters. To this end, we propose a method called Iterative Minimum Entropy (IME) to correct initial transformation parameters based on two criteria: the difference between the average and minimum entropy and the deviation from the minimum entropy to the expected entropy. Finally, the presented method is evaluated using two data sets that contain tens of millions of points from panoramic and non-panoramic, vegetation-dominated and building-dominated cases and can achieve high accuracy and efficiency.http://www.mdpi.com/1424-8220/17/1/197terrestrial laser scanningregistrationsensor combinationpoint cloudinformation entropy |
| spellingShingle | Maolin Chen Siying Wang Mingwei Wang Youchuan Wan Peipei He Entropy-Based Registration of Point Clouds Using Terrestrial Laser Scanning and Smartphone GPS Sensors terrestrial laser scanning registration sensor combination point cloud information entropy |
| title | Entropy-Based Registration of Point Clouds Using Terrestrial Laser Scanning and Smartphone GPS |
| title_full | Entropy-Based Registration of Point Clouds Using Terrestrial Laser Scanning and Smartphone GPS |
| title_fullStr | Entropy-Based Registration of Point Clouds Using Terrestrial Laser Scanning and Smartphone GPS |
| title_full_unstemmed | Entropy-Based Registration of Point Clouds Using Terrestrial Laser Scanning and Smartphone GPS |
| title_short | Entropy-Based Registration of Point Clouds Using Terrestrial Laser Scanning and Smartphone GPS |
| title_sort | entropy based registration of point clouds using terrestrial laser scanning and smartphone gps |
| topic | terrestrial laser scanning registration sensor combination point cloud information entropy |
| url | http://www.mdpi.com/1424-8220/17/1/197 |
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