Where Am I? SLAM for Mobile Machines on a Smart Working Site
The current optimization approaches of construction machinery are mainly based on internal sensors. However, the decision of a reasonable strategy is not only determined by its intrinsic signals, but also very strongly by environmental information, especially the terrain. Due to the dynamic changing...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-05-01
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| Series: | Vehicles |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2624-8921/4/2/31 |
| _version_ | 1797481541066031104 |
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| author | Yusheng Xiang Dianzhao Li Tianqing Su Quan Zhou Christine Brach Samuel S. Mao Marcus Geimer |
| author_facet | Yusheng Xiang Dianzhao Li Tianqing Su Quan Zhou Christine Brach Samuel S. Mao Marcus Geimer |
| author_sort | Yusheng Xiang |
| collection | DOAJ |
| description | The current optimization approaches of construction machinery are mainly based on internal sensors. However, the decision of a reasonable strategy is not only determined by its intrinsic signals, but also very strongly by environmental information, especially the terrain. Due to the dynamic changing of the construction site and the consequent absence of a high definition map, the Simultaneous Localization and Mapping (SLAM) offering the terrain information for construction machines is still challenging. Current SLAM technologies proposed for mobile machines are strongly dependent on costly or computationally expensive sensors, such as RTK GPS and cameras, so that commercial use is rare. In this study, we proposed an affordable SLAM method to create a multi-layer grid map for the construction site so that the machine can have the environmental information and be optimized accordingly. Concretely, after the machine passes by the grid, we can obtain the local information and record it. Combining with positioning technology, we then create a map of the interesting places of the construction site. As a result of our research gathered from Gazebo, we showed that a suitable layout is the combination of one IMU and two differential GPS antennas using the unscented Kalman filter, which keeps the average distance error lower than 2m and the mapping error lower than 1.3% in the harsh environment. As an outlook, our SLAM technology provides the cornerstone to activate many efficiency improvement approaches. |
| first_indexed | 2024-03-09T22:16:04Z |
| format | Article |
| id | doaj.art-3c353984d8bb47f9916250c7b48584e2 |
| institution | Directory Open Access Journal |
| issn | 2624-8921 |
| language | English |
| last_indexed | 2024-03-09T22:16:04Z |
| publishDate | 2022-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Vehicles |
| spelling | doaj.art-3c353984d8bb47f9916250c7b48584e22023-11-23T19:22:41ZengMDPI AGVehicles2624-89212022-05-014252955210.3390/vehicles4020031Where Am I? SLAM for Mobile Machines on a Smart Working SiteYusheng Xiang0Dianzhao Li1Tianqing Su2Quan Zhou3Christine Brach4Samuel S. Mao5Marcus Geimer6Institute of Vehicle System Technology, Karlsruhe Institute of Technology, 76133 Karlsruhe, GermanyInstitute of Vehicle System Technology, Karlsruhe Institute of Technology, 76133 Karlsruhe, GermanyGuanghua School of Management, Peking University, Beijing 100091, ChinaVehicle and Engine Research Centre, University of Birmingham, Birmingham B15 2SQ, UKDivision of Mobile Hydraulics, Robert Bosch GmbH, 89275 Elchingen, GermanyDepartment of Mechanical Engineering, University of California, Berkeley, CA 94720, USAInstitute of Vehicle System Technology, Karlsruhe Institute of Technology, 76133 Karlsruhe, GermanyThe current optimization approaches of construction machinery are mainly based on internal sensors. However, the decision of a reasonable strategy is not only determined by its intrinsic signals, but also very strongly by environmental information, especially the terrain. Due to the dynamic changing of the construction site and the consequent absence of a high definition map, the Simultaneous Localization and Mapping (SLAM) offering the terrain information for construction machines is still challenging. Current SLAM technologies proposed for mobile machines are strongly dependent on costly or computationally expensive sensors, such as RTK GPS and cameras, so that commercial use is rare. In this study, we proposed an affordable SLAM method to create a multi-layer grid map for the construction site so that the machine can have the environmental information and be optimized accordingly. Concretely, after the machine passes by the grid, we can obtain the local information and record it. Combining with positioning technology, we then create a map of the interesting places of the construction site. As a result of our research gathered from Gazebo, we showed that a suitable layout is the combination of one IMU and two differential GPS antennas using the unscented Kalman filter, which keeps the average distance error lower than 2m and the mapping error lower than 1.3% in the harsh environment. As an outlook, our SLAM technology provides the cornerstone to activate many efficiency improvement approaches.https://www.mdpi.com/2624-8921/4/2/31unscented Kalman filterlocalization of construction machinessmart working siteSLAMROS |
| spellingShingle | Yusheng Xiang Dianzhao Li Tianqing Su Quan Zhou Christine Brach Samuel S. Mao Marcus Geimer Where Am I? SLAM for Mobile Machines on a Smart Working Site Vehicles unscented Kalman filter localization of construction machines smart working site SLAM ROS |
| title | Where Am I? SLAM for Mobile Machines on a Smart Working Site |
| title_full | Where Am I? SLAM for Mobile Machines on a Smart Working Site |
| title_fullStr | Where Am I? SLAM for Mobile Machines on a Smart Working Site |
| title_full_unstemmed | Where Am I? SLAM for Mobile Machines on a Smart Working Site |
| title_short | Where Am I? SLAM for Mobile Machines on a Smart Working Site |
| title_sort | where am i slam for mobile machines on a smart working site |
| topic | unscented Kalman filter localization of construction machines smart working site SLAM ROS |
| url | https://www.mdpi.com/2624-8921/4/2/31 |
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