Obstacle Detection in Hybrid Cross-Country Environment Based on Markov Random Field for Unmanned Ground Vehicle
In order to detect the obstacle from the large amount of 3D LIDAR data in hybrid cross-country environment for unmanned ground vehicle, a new graph approach based on Markov random field was presented. Firstly, the preprocessing method based on the maximum blurred line is applied to segment the proje...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2015-01-01
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| Series: | Discrete Dynamics in Nature and Society |
| Online Access: | http://dx.doi.org/10.1155/2015/540968 |
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| _version_ | 1850179190451601408 |
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| author | Feng Ding Yibing Zhao Lie Guo Mingheng Zhang Linhui Li |
| author_facet | Feng Ding Yibing Zhao Lie Guo Mingheng Zhang Linhui Li |
| author_sort | Feng Ding |
| collection | DOAJ |
| description | In order to detect the obstacle from the large amount of 3D LIDAR data in hybrid cross-country environment for unmanned ground vehicle, a new graph approach based on Markov random field was presented. Firstly, the preprocessing method based on the maximum blurred line is applied to segment the projection of every laser scan line in x-y plane. Then, based on K-means clustering algorithm, the same properties of the line are combined. Secondly, line segment nodes are precisely positioned by using corner detection method, and the next step is to take advantage of line segment nodes to build an undirected graph for Markov random field. Lastly, the energy function is calculated by means of analyzing line segment features and solved by graph cut. Two types of line mark are finally classified into two categories: ground and obstacle. Experiments prove the feasibility of the approach and show that it has better performance and runs in real time. |
| format | Article |
| id | doaj-art-be2a3ddf5bfc4b7f8bbb15b996e1bfe5 |
| institution | OA Journals |
| issn | 1026-0226 1607-887X |
| language | English |
| publishDate | 2015-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Discrete Dynamics in Nature and Society |
| spelling | doaj-art-be2a3ddf5bfc4b7f8bbb15b996e1bfe52025-08-20T02:18:34ZengWileyDiscrete Dynamics in Nature and Society1026-02261607-887X2015-01-01201510.1155/2015/540968540968Obstacle Detection in Hybrid Cross-Country Environment Based on Markov Random Field for Unmanned Ground VehicleFeng Ding0Yibing Zhao1Lie Guo2Mingheng Zhang3Linhui Li4School of Software, Dalian University of Technology, Liaoning 116024, ChinaState Key Laboratory of Structural Analysis for Industrial Equipment, Department of Vehicle Engineering, Dalian University of Technology, Dalian 116024, ChinaState Key Laboratory of Structural Analysis for Industrial Equipment, Department of Vehicle Engineering, Dalian University of Technology, Dalian 116024, ChinaState Key Laboratory of Structural Analysis for Industrial Equipment, Department of Vehicle Engineering, Dalian University of Technology, Dalian 116024, ChinaState Key Laboratory of Structural Analysis for Industrial Equipment, Department of Vehicle Engineering, Dalian University of Technology, Dalian 116024, ChinaIn order to detect the obstacle from the large amount of 3D LIDAR data in hybrid cross-country environment for unmanned ground vehicle, a new graph approach based on Markov random field was presented. Firstly, the preprocessing method based on the maximum blurred line is applied to segment the projection of every laser scan line in x-y plane. Then, based on K-means clustering algorithm, the same properties of the line are combined. Secondly, line segment nodes are precisely positioned by using corner detection method, and the next step is to take advantage of line segment nodes to build an undirected graph for Markov random field. Lastly, the energy function is calculated by means of analyzing line segment features and solved by graph cut. Two types of line mark are finally classified into two categories: ground and obstacle. Experiments prove the feasibility of the approach and show that it has better performance and runs in real time.http://dx.doi.org/10.1155/2015/540968 |
| spellingShingle | Feng Ding Yibing Zhao Lie Guo Mingheng Zhang Linhui Li Obstacle Detection in Hybrid Cross-Country Environment Based on Markov Random Field for Unmanned Ground Vehicle Discrete Dynamics in Nature and Society |
| title | Obstacle Detection in Hybrid Cross-Country Environment Based on Markov Random Field for Unmanned Ground Vehicle |
| title_full | Obstacle Detection in Hybrid Cross-Country Environment Based on Markov Random Field for Unmanned Ground Vehicle |
| title_fullStr | Obstacle Detection in Hybrid Cross-Country Environment Based on Markov Random Field for Unmanned Ground Vehicle |
| title_full_unstemmed | Obstacle Detection in Hybrid Cross-Country Environment Based on Markov Random Field for Unmanned Ground Vehicle |
| title_short | Obstacle Detection in Hybrid Cross-Country Environment Based on Markov Random Field for Unmanned Ground Vehicle |
| title_sort | obstacle detection in hybrid cross country environment based on markov random field for unmanned ground vehicle |
| url | http://dx.doi.org/10.1155/2015/540968 |
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