Solution to the SLAM Problem in Low Dynamic Environments Using a Pose Graph and an RGB-D Sensor
In this study, we propose a solution to the simultaneous localization and mapping (SLAM) problem in low dynamic environments by using a pose graph and an RGB-D (red-green-blue depth) sensor. The low dynamic environments refer to situations in which the positions of objects change over long intervals...
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| Format: | Article |
| Language: | English |
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MDPI AG
2014-07-01
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| Series: | Sensors |
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| Online Access: | http://www.mdpi.com/1424-8220/14/7/12467 |
| _version_ | 1828351196176318464 |
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| author | Donghwa Lee Hyun Myung |
| author_facet | Donghwa Lee Hyun Myung |
| author_sort | Donghwa Lee |
| collection | DOAJ |
| description | In this study, we propose a solution to the simultaneous localization and mapping (SLAM) problem in low dynamic environments by using a pose graph and an RGB-D (red-green-blue depth) sensor. The low dynamic environments refer to situations in which the positions of objects change over long intervals. Therefore, in the low dynamic environments, robots have difficulty recognizing the repositioning of objects unlike in highly dynamic environments in which relatively fast-moving objects can be detected using a variety of moving object detection algorithms. The changes in the environments then cause groups of false loop closing when the same moved objects are observed for a while, which means that conventional SLAM algorithms produce incorrect results. To address this problem, we propose a novel SLAM method that handles low dynamic environments. The proposed method uses a pose graph structure and an RGB-D sensor. First, to prune the falsely grouped constraints efficiently, nodes of the graph, that represent robot poses, are grouped according to the grouping rules with noise covariances. Next, false constraints of the pose graph are pruned according to an error metric based on the grouped nodes. The pose graph structure is reoptimized after eliminating the false information, and the corrected localization and mapping results are obtained. The performance of the method was validated in real experiments using a mobile robot system. |
| first_indexed | 2024-04-14T01:35:59Z |
| format | Article |
| id | doaj.art-b89635081cfb4885abb0489032694dfe |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-14T01:35:59Z |
| publishDate | 2014-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-b89635081cfb4885abb0489032694dfe2022-12-22T02:19:56ZengMDPI AGSensors1424-82202014-07-01147124671249610.3390/s140712467s140712467Solution to the SLAM Problem in Low Dynamic Environments Using a Pose Graph and an RGB-D SensorDonghwa Lee0Hyun Myung1Urban Robotics Laboratory, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon 305-701, KoreaUrban Robotics Laboratory, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon 305-701, KoreaIn this study, we propose a solution to the simultaneous localization and mapping (SLAM) problem in low dynamic environments by using a pose graph and an RGB-D (red-green-blue depth) sensor. The low dynamic environments refer to situations in which the positions of objects change over long intervals. Therefore, in the low dynamic environments, robots have difficulty recognizing the repositioning of objects unlike in highly dynamic environments in which relatively fast-moving objects can be detected using a variety of moving object detection algorithms. The changes in the environments then cause groups of false loop closing when the same moved objects are observed for a while, which means that conventional SLAM algorithms produce incorrect results. To address this problem, we propose a novel SLAM method that handles low dynamic environments. The proposed method uses a pose graph structure and an RGB-D sensor. First, to prune the falsely grouped constraints efficiently, nodes of the graph, that represent robot poses, are grouped according to the grouping rules with noise covariances. Next, false constraints of the pose graph are pruned according to an error metric based on the grouped nodes. The pose graph structure is reoptimized after eliminating the false information, and the corrected localization and mapping results are obtained. The performance of the method was validated in real experiments using a mobile robot system.http://www.mdpi.com/1424-8220/14/7/12467simultaneous localization and mapping (SLAM)low dynamic environmentpose graphRGB-D (red-green-blue depth) |
| spellingShingle | Donghwa Lee Hyun Myung Solution to the SLAM Problem in Low Dynamic Environments Using a Pose Graph and an RGB-D Sensor Sensors simultaneous localization and mapping (SLAM) low dynamic environment pose graph RGB-D (red-green-blue depth) |
| title | Solution to the SLAM Problem in Low Dynamic Environments Using a Pose Graph and an RGB-D Sensor |
| title_full | Solution to the SLAM Problem in Low Dynamic Environments Using a Pose Graph and an RGB-D Sensor |
| title_fullStr | Solution to the SLAM Problem in Low Dynamic Environments Using a Pose Graph and an RGB-D Sensor |
| title_full_unstemmed | Solution to the SLAM Problem in Low Dynamic Environments Using a Pose Graph and an RGB-D Sensor |
| title_short | Solution to the SLAM Problem in Low Dynamic Environments Using a Pose Graph and an RGB-D Sensor |
| title_sort | solution to the slam problem in low dynamic environments using a pose graph and an rgb d sensor |
| topic | simultaneous localization and mapping (SLAM) low dynamic environment pose graph RGB-D (red-green-blue depth) |
| url | http://www.mdpi.com/1424-8220/14/7/12467 |
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