BotanicGarden: A High-Quality Dataset for Robot Navigation in Unstructured Natural Environments

doi:10.1109/LRA.2024.3359548

	BotanicGarden: A High-Quality Dataset for Robot Navigation in Unstructured Natural Environments
	Liu, Yuanzhi 1; Fu, Yujia 1; Qin, Minghui 1; Xu, Yufeng 1; Xu, Baoxin 1; Chen, Fengdong 2; Goossens, Bart 3; Sun, Poly Z. H.4; Yu, Hongwei 5; Liu, Chun 6; Chen, Long7 ; Tao, Wei 1; Zhao, Hui 1
发表期刊	IEEE ROBOTICS AND AUTOMATION LETTERS
ISSN	2377-3766
	2024-03-01
卷号	9 期号:3 页码:2798-2805
通讯作者	Goossens, Bart(bart.goossens@ugent.be) ; Zhao, Hui(huizhao@sjtu.edu.cn)
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.
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
DOI	10.1109/LRA.2024.3359548
关键词[WOS]	DATA SET ; LOCALIZATION ; MULTISENSOR ; VERSATILE ; VEHICLES ; ROBUST ; SLAM
收录类别	SCI
语种	英语
资助项目	National Key Ramp;D Program of China
项目资助者	National Key Ramp;D Program of China
WOS研究方向	Robotics
WOS类目	Robotics
WOS记录号	WOS:001174297500015
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/56934
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.Harbin Inst Technol, Sch Instrumentat, Harbin 150001, Peoples R China 3.imec IPI Ghent Univ, B-9000 Ghent, Belgium 4.Shanghai Jiao Tong Univ, Sch Mech Engn, Shanghai 200240, Peoples R China 5.Chinese Aeronaut Radio Elect Res Inst, Shanghai 200233, Peoples R China 6.Tongji Univ, Coll Surveying & Geoinformat, Shanghai 200092, Peoples R China 7.Chinese Acad Sci, Inst Automat, Beijing 100190, 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.