IGE-LIO: Intensity Gradient Enhanced Tightly Coupled LiDAR-Inertial Odometry
文献类型：期刊论文
| 作者 | Chen, Ziyu1; Zhu, Hui2 ; Yu, Biao2; Jiang, Chunmao1; Hua, Chen1; Fu, Xuhui1; Kuang, Xinkai1
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| 刊名 | IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
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| 出版日期 | 2024 |
| 卷号 | 73 |
| 关键词 | Laser radar Feature extraction Simultaneous localization and mapping Noise Accuracy Location awareness Data mining Degenerated environments intensity gradient localization simultaneous localization and mapping (SLAM) weighting function |
| ISSN号 | 0018-9456 |
| DOI | 10.1109/TIM.2024.3427795 |
| 通讯作者 | Zhu, Hui(hzhu@iim.ac.cn) |
| 英文摘要 | Simultaneous localization and mapping (SLAM) plays an important role in the state estimation of mobile robots. Most popular LiDAR SLAM (L-SLAM) methods extract feature points only from the geometric structure of the environment, which can result in inaccurate localization in degenerated scenarios. In this article, we present a novel framework for LiDAR intensity gradient enhanced tightly coupled LiDAR-inertial odometry (IGE-LIO). The framework proposes a novel LiDAR intensity gradient-based feature extraction approach for accurate pose estimation, overcoming the challenges faced by L-SLAM in degenerated environments. After computing the intensity gradient of each LiDAR point, we dynamically extract intensity edge points (IEPs) from texture information. In addition, we extract geometric planar points (GPPs) and geometric edge points (GEPs) based on geometric information. Then, the error analysis is performed on each type of feature points, and the weighting functions are designed to correct measurement noise and mitigate biases introduced by the additional uncertainty in feature extraction. Subsequently, an iterative extended Kalman filter (IEKF) framework is constructed by combining residuals from point-to-plane and point-to-edge associations. Finally, extensive experiments are conducted in indoor, outdoor, and LiDAR degenerated scenarios. The results demonstrate the significantly improved robustness and accuracy of our proposed method compared with the existing geometric-only methods, especially in LiDAR degenerated scenarios. |
| WOS关键词 | ROBUST |
| 资助项目 | Youth Innovation Promotion Association of Chinese Academy of Sciences (CAS)[Y2021115] ; Dreams Foundation of Jianghuai Advance Technology Center[2023-ZM01G002] ; Anhui Province Key Research and Development Plan[202304a05020065] |
| WOS研究方向 | Engineering ; Instruments & Instrumentation |
| 语种 | 英语 |
| WOS记录号 | WOS:001301007700005 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 资助机构 | Youth Innovation Promotion Association of Chinese Academy of Sciences (CAS) ; Dreams Foundation of Jianghuai Advance Technology Center ; Anhui Province Key Research and Development Plan |
| 源URL | [http://ir.hfcas.ac.cn:8080/handle/334002/135002]  |
| 专题 | 中国科学院合肥物质科学研究院 |
| 通讯作者 | Zhu, Hui |
| 作者单位 | 1.Chinese Acad Sci, Hefei Inst Phys Sci, Hefei 230031, Anhui, Peoples R China 2.Chinese Acad Sci, Hefei Inst Phys Sci, Hefei 230031, Anhui, Peoples R China |
| 推荐引用方式 GB/T 7714 | Chen, Ziyu,Zhu, Hui,Yu, Biao,et al. IGE-LIO: Intensity Gradient Enhanced Tightly Coupled LiDAR-Inertial Odometry[J]. IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT,2024,73. |
| APA | Chen, Ziyu.,Zhu, Hui.,Yu, Biao.,Jiang, Chunmao.,Hua, Chen.,...&Kuang, Xinkai.(2024).IGE-LIO: Intensity Gradient Enhanced Tightly Coupled LiDAR-Inertial Odometry.IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT,73. |
| MLA | Chen, Ziyu,et al."IGE-LIO: Intensity Gradient Enhanced Tightly Coupled LiDAR-Inertial Odometry".IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 73(2024). |
入库方式： OAI收割
来源：合肥物质科学研究院
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