GR-Fusion: Multi-sensor Fusion SLAM for Ground Robots with High Robustness and Low Drift
文献类型：会议论文
| 作者 | Wang T(王挺)2,3 ; Su Y(苏赟)1,2,3,5; Shao SL(邵士亮)2,3; Yao C(姚辰)2,3; Wang ZD(王志东)4
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| 出版日期 | 2021 |
| 会议日期 | September 27 - October 1, 2021 |
| 会议地点 | Prague, Czech republic |
| 页码 | 5440-5447 |
| 英文摘要 | This paper presents a tightly coupled pipeline, which efficiently fuses measurements of LiDAR, camera, IMU, encoder, and GNSS to estimate the robot state and build a map even in challenging situations. The depth of visual features is extracted by projecting the LiDAR point cloud and ground plane into image. We select the tracked high-quality visual features and LiDAR features and tightly coupled the pre-integrated values of the IMU and the encoder to optimize the state increment of a robot. We use the estimated relative pose to re-evaluate the matching distance between features in the local window and remove dynamic objects and outliers. In the mapping node, we use refined features and tightly coupled the GNSS measurements, increment factors, and local ground constraints to further refine the robot's global state by aligning LiDAR features with the global map. Furthermore, the method can detect sensor degradation and automatically reconfigure the optimization process. Based on a six-wheeled ground robot, we perform extensive experiments in both indoor and outdoor environments and demonstrated that the proposed GR-Fusion outperforms state-of-the-art SLAM methods in terms of accuracy and robustness. |
| 产权排序 | 1 |
| 会议录 | IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2021
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| 会议录出版者 | IEEE |
| 会议录出版地 | New York |
| 语种 | 英语 |
| ISSN号 | 2153-0858 |
| ISBN号 | 978-1-6654-1714-3 |
| WOS记录号 | WOS:000755125504048 |
| 源URL | [http://ir.sia.cn/handle/173321/30496]  |
| 专题 | 沈阳自动化研究所_机器人学研究室 |
| 通讯作者 | Su Y(苏赟) |
| 作者单位 | 1.University of Chinese Academy of Sciences, Beijing 100049, China 2.Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110016, China 3.State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China 4..Department of Advanced Robotics, Chiba Institute of Technology, Chiba, Japan 5.Guangzhou Shiyuan Electronic Technology Company Limited, Guangzhou, China |
| 推荐引用方式 GB/T 7714 | Wang T,Su Y,Shao SL,et al. GR-Fusion: Multi-sensor Fusion SLAM for Ground Robots with High Robustness and Low Drift[C]. 见:. Prague, Czech republic. September 27 - October 1, 2021. |
入库方式： OAI收割
来源：沈阳自动化研究所
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