BotanicGarden: A High-Quality Dataset for Robot Navigation in Unstructured Natural Environments
文献类型：期刊论文
| 作者 | Liu, Yuanzhi1; Fu, Yujia1; Qin, Minghui1; Xu, Yufeng1; Xu, Baoxin1; Chen, Fengdong7; Goossens, Bart6; Sun, Poly Z. H.5; Yu, Hongwei4; Liu, Chun3 |
| 刊名 | IEEE ROBOTICS AND AUTOMATION LETTERS
 |
| 出版日期 | 2024-03-01 |
| 卷号 | 9期号:3页码:2798-2805 |
| 关键词 | Robots Navigation Simultaneous localization and mapping Three-dimensional displays Global navigation satellite system Electronic mail Laser radar Data sets for SLAM field robots data sets for robotic vision navigation unstructured environments |
| ISSN号 | 2377-3766 |
| DOI | 10.1109/LRA.2024.3359548 |
| 通讯作者 | Goossens, Bart(bart.goossens@ugent.be) ; Zhao, Hui(huizhao@sjtu.edu.cn) |
| 英文摘要 | The rapid developments of mobile robotics and autonomous navigation over the years are largely empowered by public datasets for testing and upgrading, such as sensor odometry and SLAM tasks. Impressive demos and benchmark scores have arisen, which may suggest the maturity of existing navigation techniques. However, these results are primarily based on moderate structured scenario testing. When transitioning to challenging unstructured environments, especially in GNSS-denied, texture-monotonous, and dense-vegetated natural fields, their performance can hardly sustain at a high level and requires further validation and improvement. To bridge this gap, we build a novel robot navigation dataset in a luxuriant botanic garden of more than 48000 m(2). Comprehensive sensors are used, including Gray and RGB stereo cameras, spinning and MEMS 3D LiDARs, and low-cost and industrial-grade IMUs, all of which are well calibrated and hardware-synchronized. An all-terrain wheeled robot is employed for data collection, traversing through thick woods, riversides, narrow trails, bridges, and grasslands, which are scarce in previous resources. This yields 33 short and long sequences, forming 17.1 km trajectories in total. Excitedly, both highly-accurate ego-motions and 3D map ground truth are provided, along with fine-annotated vision semantics. We firmly believe that our dataset can advance robot navigation and sensor fusion research to a higher level. |
| WOS关键词 | DATA SET ; LOCALIZATION ; MULTISENSOR ; VERSATILE ; VEHICLES ; ROBUST ; SLAM |
| 资助项目 | National Key Ramp;D Program of China |
| WOS研究方向 | Robotics |
| 语种 | 英语 |
| WOS记录号 | WOS:001174297500015 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 资助机构 | National Key Ramp;D Program of China |
| 源URL | [http://ir.ia.ac.cn/handle/173211/56933]  |
| 专题 | 多模态人工智能系统全国重点实验室 |
| 通讯作者 | Goossens, Bart; Zhao, Hui |
| 作者单位 | 1.Shanghai Jiao Tong Univ, Sch Sensing Sci & Engn, Shanghai 200240, Peoples R China 2.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China 3.Tongji Univ, Coll Surveying & Geoinformat, Shanghai 200092, Peoples R China 4.Chinese Aeronaut Radio Elect Res Inst, Shanghai 200233, Peoples R China 5.Shanghai Jiao Tong Univ, Sch Mech Engn, Shanghai 200240, Peoples R China 6.imec IPI Ghent Univ, B-9000 Ghent, Belgium 7.Harbin Inst Technol, Sch Instrumentat, Harbin 150001, Peoples R China |
| 推荐引用方式 GB/T 7714 | Liu, Yuanzhi,Fu, Yujia,Qin, Minghui,et al. BotanicGarden: A High-Quality Dataset for Robot Navigation in Unstructured Natural Environments[J]. IEEE ROBOTICS AND AUTOMATION LETTERS,2024,9(3):2798-2805. |
| APA | Liu, Yuanzhi.,Fu, Yujia.,Qin, Minghui.,Xu, Yufeng.,Xu, Baoxin.,...&Zhao, Hui.(2024).BotanicGarden: A High-Quality Dataset for Robot Navigation in Unstructured Natural Environments.IEEE ROBOTICS AND AUTOMATION LETTERS,9(3),2798-2805. |
| MLA | Liu, Yuanzhi,et al."BotanicGarden: A High-Quality Dataset for Robot Navigation in Unstructured Natural Environments".IEEE ROBOTICS AND AUTOMATION LETTERS 9.3(2024):2798-2805. |
入库方式： OAI收割
来源：自动化研究所
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