A 3D Lidar SLAM System Based on Semantic Segmentation for Rubber-Tapping Robot
Simultaneous localization and mapping (SLAM) in rubber plantations is a challenging task for rubber-tapping robots. Due to the long-term stability of tree trunks in rubber plantations, a SLAM system based on semantic segmentation, called Se-LOAM, is proposed in this work. The 3D lidar point cloud da...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-09-01
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| Series: | Forests |
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| Online Access: | https://www.mdpi.com/1999-4907/14/9/1856 |
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| author | Hui Yang Yaya Chen Junxiao Liu Zhifu Zhang Xirui Zhang |
| author_facet | Hui Yang Yaya Chen Junxiao Liu Zhifu Zhang Xirui Zhang |
| author_sort | Hui Yang |
| collection | DOAJ |
| description | Simultaneous localization and mapping (SLAM) in rubber plantations is a challenging task for rubber-tapping robots. Due to the long-term stability of tree trunks in rubber plantations, a SLAM system based on semantic segmentation, called Se-LOAM, is proposed in this work. The 3D lidar point cloud datasets of trunks collected in rubber plantations of Hainan University are used to train the semantic model, and the model is used to extract features of trunk point clouds. After clustering the trunk point clouds, each single rubber tree instance is segmented based on the Viterbi algorithm. The point clouds of tree instances are fitted to the cylindrical trunk models for semantic cluster association and positional estimation, which are used for lidar odometry and mapping. The experimental results show that the present SLAM system is accurate in establishing online mapping, and the location of the trunk in the map is clearer. Specifically, the average relative pose error is 0.02 m, which is better than the positioning performance of LOAM and LeGO-LOAM. The average error of estimating the diameter at breast height (DBH) is 0.57 cm, and it only takes 401.4 kB to store a map of the area of approximately 500 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mrow><mi mathvariant="normal">m</mi></mrow><mrow><mn>2</mn></mrow></msup></mrow></semantics></math></inline-formula>, which is about 10% less than other classic methods. Therefore, Se-LOAM can meet the requirements of online mapping, providing a robust SLAM method for rubber-tapping robots. |
| first_indexed | 2024-03-10T22:44:05Z |
| format | Article |
| id | doaj.art-2e22e153102e4db7b7922837ecf6156b |
| institution | Directory Open Access Journal |
| issn | 1999-4907 |
| language | English |
| last_indexed | 2024-03-10T22:44:05Z |
| publishDate | 2023-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Forests |
| spelling | doaj.art-2e22e153102e4db7b7922837ecf6156b2023-11-19T10:46:59ZengMDPI AGForests1999-49072023-09-01149185610.3390/f14091856A 3D Lidar SLAM System Based on Semantic Segmentation for Rubber-Tapping RobotHui Yang0Yaya Chen1Junxiao Liu2Zhifu Zhang3Xirui Zhang4Mechanical and Electrical Engineering College, Hainan University, Haikou 570228, ChinaMechanical and Electrical Engineering College, Hainan University, Haikou 570228, ChinaMechanical and Electrical Engineering College, Hainan University, Haikou 570228, ChinaMechanical and Electrical Engineering College, Hainan University, Haikou 570228, ChinaMechanical and Electrical Engineering College, Hainan University, Haikou 570228, ChinaSimultaneous localization and mapping (SLAM) in rubber plantations is a challenging task for rubber-tapping robots. Due to the long-term stability of tree trunks in rubber plantations, a SLAM system based on semantic segmentation, called Se-LOAM, is proposed in this work. The 3D lidar point cloud datasets of trunks collected in rubber plantations of Hainan University are used to train the semantic model, and the model is used to extract features of trunk point clouds. After clustering the trunk point clouds, each single rubber tree instance is segmented based on the Viterbi algorithm. The point clouds of tree instances are fitted to the cylindrical trunk models for semantic cluster association and positional estimation, which are used for lidar odometry and mapping. The experimental results show that the present SLAM system is accurate in establishing online mapping, and the location of the trunk in the map is clearer. Specifically, the average relative pose error is 0.02 m, which is better than the positioning performance of LOAM and LeGO-LOAM. The average error of estimating the diameter at breast height (DBH) is 0.57 cm, and it only takes 401.4 kB to store a map of the area of approximately 500 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mrow><mi mathvariant="normal">m</mi></mrow><mrow><mn>2</mn></mrow></msup></mrow></semantics></math></inline-formula>, which is about 10% less than other classic methods. Therefore, Se-LOAM can meet the requirements of online mapping, providing a robust SLAM method for rubber-tapping robots.https://www.mdpi.com/1999-4907/14/9/1856SLAMrubber-tapping robotssemantic segmentation3D lidarpoint clouds |
| spellingShingle | Hui Yang Yaya Chen Junxiao Liu Zhifu Zhang Xirui Zhang A 3D Lidar SLAM System Based on Semantic Segmentation for Rubber-Tapping Robot Forests SLAM rubber-tapping robots semantic segmentation 3D lidar point clouds |
| title | A 3D Lidar SLAM System Based on Semantic Segmentation for Rubber-Tapping Robot |
| title_full | A 3D Lidar SLAM System Based on Semantic Segmentation for Rubber-Tapping Robot |
| title_fullStr | A 3D Lidar SLAM System Based on Semantic Segmentation for Rubber-Tapping Robot |
| title_full_unstemmed | A 3D Lidar SLAM System Based on Semantic Segmentation for Rubber-Tapping Robot |
| title_short | A 3D Lidar SLAM System Based on Semantic Segmentation for Rubber-Tapping Robot |
| title_sort | 3d lidar slam system based on semantic segmentation for rubber tapping robot |
| topic | SLAM rubber-tapping robots semantic segmentation 3D lidar point clouds |
| url | https://www.mdpi.com/1999-4907/14/9/1856 |
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