Ground-Level Mapping and Navigating for Agriculture Based on IoT and Computer Vision

Zhao, Wei; Wang, Xuan; Qi, Bozhao; Runge, Troy

doi:10.1109/access.2020.3043662

Cited by 41 publications

(11 citation statements)

References 32 publications

(28 reference statements)

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“…Therefore, some groups put 3D mapping tasks in the cloud. W. Zhao et al (2020) proposed a ground-level mapping and navigating system based on computer vision technology (Mesh Simultaneous Localization and Mapping algorithm, Mesh-SLAM) and Internet of Things, to generate a 3D farm map on both the edge side and cloud. In the system, high efficiency and speed of mapping stage are enabled by making the robot vehicles directly stream continuous frames to their corresponding edge node.…”

Section: Applications Of Slam For Mappingmentioning

confidence: 99%

Recent developments and applications of simultaneous localization and mapping in agriculture

Ding

Zhang

Zhou

et al. 2022

Journal of Field Robotics

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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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Section: Applications Of Slam For Mappingmentioning

confidence: 99%

Recent developments and applications of simultaneous localization and mapping in agriculture

Ding

Zhang

Zhou

et al. 2022

Journal of Field Robotics

View full text Add to dashboard Cite

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“…Several considerations must be pondered while deploying IoT systems in livestock. These considerations are IoT devices' capacity to accept the play capability and position that must be installed and linked anywhere in livestock farms without the requirement of gateways [137].…”

Section: ) Localizationmentioning

confidence: 99%

A Survey on the Role of IoT in Agriculture for the Implementation of Smart Livestock Environment

et al. 2022

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The Internet of Things (IoT) is an emerging paradigm that is transforming real-world things (objects) into smarter devices. IoT is applicable to a variety of application domains including healthcare, smart grid, and agriculture. This domain has started revolutionizing the agriculture industry by providing smart solutions for precision farming, greenhouse management, and livestock monitoring. This article aims to present a comprehensive survey on the role of IoT in the Livestock field by categorizing and synthesizing existing research work in this area. To this end, a detailed discussion has been provided on IoT network infrastructure, topologies and platforms employed for livestock management. In addition, a list of communication protocols and connections of IoT-based livestock systems with relevant technologies have also been explored. Furthermore, numerous IoT-based livestock monitoring, controlling, and tracking applications have been discussed. Apart from this, it also analyses distinct security issues in IoT-based livestock field and developed a collaborative security model to detect and minimize the security risk. Lastly, pertinent open research challenges in the domain of IoT-based livestock management have been presented with future research directions.

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“…SLAM and 3D reconstruction techniques have been a hot research topic in computer vision and computer graphics for over three decades. These technologies are widely used in areas such as unmanned vehicles [1], precision agriculture [2], urban planning [3], disaster risk management [4], etc. Some areas require the framing of indoor scenes and motion tracks, and others need to draw highquality 3D models of aerial scenes.…”

Section: Introductionmentioning

confidence: 99%

A Scale-Adaptive Time-Efficient Depth Map Fusion Algorithm

Zhang¹,

Lai²,

Wang³

et al. 2023

Preprint

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TSDF (truncated signed distance function) is a classical algorithm for real-time 3D reconstruction, which is simple and parallelizable. Still, the memory consumption and cost of time consumption limit the expansion of the reconstructed scene. To solve these problems, we proposed a new depth map fusion system. We open up a dynamically adjustable memory space, the size of which can be adaptively adjusted according to the coordinate distribution interval obtained from the GMM(Gaussian Mixture Model). When the scene exceeds the voxel coverage, a loop switch is triggered, and the mode of recycling the cache space is turned on. This system has a scene-adaptive memory management mechanism interspersed with CPU-GPU cooperative operations. It can improve the time efficiency without sacrificing the reconstruction accuracy and make it possible to reconstruct high-scale changing scenes. We have created a dataset of natural aviation scenes for extensive experiments. The experimental results show that our system is suitable for any scale of scenes. In terms of time, our method improves the time to fuse each depth map frame by order of magnitude. In evaluating the quality of the reconstructed surface, we compared our reconstructed surfaces with commercial 3D software, and the results show that more than $92 \%$ of the fractions differed from Metashape's results by less than 0.6m. In general, our system is more suitable for scenes with dynamic changes in scene scale and faster task requirements, which will promote the expansion of 3D reconstruction in practical applications.

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Ground-Level Mapping and Navigating for Agriculture Based on IoT and Computer Vision

Cited by 41 publications

References 32 publications

Recent developments and applications of simultaneous localization and mapping in agriculture

Recent developments and applications of simultaneous localization and mapping in agriculture

A Survey on the Role of IoT in Agriculture for the Implementation of Smart Livestock Environment

A Scale-Adaptive Time-Efficient Depth Map Fusion Algorithm

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