期刊
JOURNAL OF FIELD ROBOTICS
卷 39, 期 6, 页码 956-983出版社
WILEY
DOI: 10.1002/rob.22077
关键词
agricultural applications; autonomous navigation; precision agriculture; sensors and systems; SLAM
类别
资金
- National Natural Science Foundation of China [31471419, 31901415]
- Jiangsu Agricultural Science and Technology Innovation Fund (JASTIF) [CX (21)3146]
The translation introduces the definition and application areas of SLAM technology, as well as its developments and prospects in agriculture. It provides a detailed overview of the fundamental types of SLAM and discusses the sensors, systems, and algorithms used in agricultural applications. Additionally, the challenges and future trends of SLAM are reported.
Simultaneous Localization and Mapping (SLAM) is a process to use multiple sensors to position an unmanned mobile vehicle without previous knowledge of the environment, and meanwhile construct a map of this environment for the further applications. Over the past three decades, SLAM has been intensively researched and widely applied in mobile robot control and unmanned vehicle navigation. SLAM technology has demonstrated a great potential in autonomously navigating the mobile robot and simultaneously reconstructing the three-dimensional (3D) information of surrounding environment. With the vigorous driving of sensor technology and 3D reconstruction algorithms, many attempts have been conducted to propose novel systems and algorithms combined with different sensors to solve the SLAM problem. Notably, SLAM has been extended to various aspects of agriculture involved with autonomous navigation, 3D mapping, field monitoring, and intelligent spraying. This paper focuses on the recent developments and applications of SLAM, particularly in complex and unstructured agricultural environment. A detailed summary of the developments of SLAM is given from three main fundamental types: light detection and ranging SLAM, Visual SLAM, and Sensor Fusion SLAM, and we also discuss the applications and prospects of SLAM technology in agricultural mapping, agricultural navigation, and precise automatic agriculture. Particular attention has been paid to the SLAM sensors, systems, and algorithms applied in agricultural tasks. Additionally, the challenges and future trends of SLAM are reported.
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