An improved LIO-SAM algorithm by integrating image information for dynamic and unstructured environments
文献类型：期刊论文
| 作者 | Meng, Xinyu2,3; Chen, Xi2,3; Chen, Shaofeng1; Fang, Yongfeng2,3; Fan, Hao2,3; Luo, Jiyu2,3; Wu, Yuhan2,3; Sun, Bingyu2,3
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| 刊名 | MEASUREMENT SCIENCE AND TECHNOLOGY
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| 出版日期 | 2024-09-01 |
| 卷号 | 35 |
| 关键词 | object-aware simultaneous localization and mapping (SLAM) dynamic scenes JPDA data association velocity integration |
| ISSN号 | 0957-0233 |
| DOI | 10.1088/1361-6501/ad56b1 |
| 通讯作者 | Sun, Bingyu(bysun@iim.ac.cn) |
| 英文摘要 | Simultaneous localization and mapping (SLAM) is the process of estimating the trajectory of a mobile sensor carrier and creating a representation of its surroundings. Traditional SLAM algorithms are based on 'static world assumption' and simplify the problem by filtering out moving objects or tracking them separately in complex dynamic environments. However, this strong assumption restricts the application of SLAM algorithms on highly dynamic and unstructured environments. In order to resolve above problem, this paper propose an improved object-aware dynamic SLAM system by integrating image information, i.e. semantic and velocity information. Firstly, we adopt deep learning method to detect both the 2D and 3D bounding boxes of objects in the environment. This information is then used to perform multi-view, multi-dimensional bundle optimization to jointly refine the poses of camera, object, and point. Secondly, 2D detection results from image and 3D detection results from lidar are integrated by the joint probabilistic data association data association algorithm to facilitate object-level data association. We also calculate 2D and 3D motion velocity and this information is used to constraint the motion of the object. Finally, we perform comprehensive experiments on different datasets, including NCLT, M2DGR, and KITTI to prove the performance of the proposed method. |
| WOS关键词 | MULTIOBJECT TRACKING ; ODOMETRY ; LIDAR ; SLAM |
| 资助项目 | National Natural Science Foundation of Chinahttp://dx.doi.org/10.13039/501100001809 |
| WOS研究方向 | Engineering ; Instruments & Instrumentation |
| 语种 | 英语 |
| WOS记录号 | WOS:001251406300001 |
| 出版者 | IOP Publishing Ltd |
| 资助机构 | National Natural Science Foundation of Chinahttp://dx.doi.org/10.13039/501100001809 |
| 源URL | [http://ir.hfcas.ac.cn:8080/handle/334002/136424]  |
| 专题 | 中国科学院合肥物质科学研究院 |
| 通讯作者 | Sun, Bingyu |
| 作者单位 | 1.Univ Sci & Technol China, Dept Automat, Hefei 230026, Peoples R China 2.Univ Sci & Technol China, Hefei 230026, Peoples R China 3.Chinese Acad Sci, Hefei Inst Phys Sci, Hefei 230031, Peoples R China |
| 推荐引用方式 GB/T 7714 | Meng, Xinyu,Chen, Xi,Chen, Shaofeng,et al. An improved LIO-SAM algorithm by integrating image information for dynamic and unstructured environments[J]. MEASUREMENT SCIENCE AND TECHNOLOGY,2024,35. |
| APA | Meng, Xinyu.,Chen, Xi.,Chen, Shaofeng.,Fang, Yongfeng.,Fan, Hao.,...&Sun, Bingyu.(2024).An improved LIO-SAM algorithm by integrating image information for dynamic and unstructured environments.MEASUREMENT SCIENCE AND TECHNOLOGY,35. |
| MLA | Meng, Xinyu,et al."An improved LIO-SAM algorithm by integrating image information for dynamic and unstructured environments".MEASUREMENT SCIENCE AND TECHNOLOGY 35(2024). |
入库方式： OAI收割
来源：合肥物质科学研究院
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