SLAM; definition and evolution

Taheri, Hassan; Xia, Zhao

doi:10.1016/j.engappai.2020.104032

Cited by 87 publications

(23 citation statements)

References 96 publications

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“…Some autonomous navigation studies in forests to date have focused on trail following (e.g., [33]), which presents unique challenges but is a distinct problem from comprehensively surveying large tracts of forests. Fortunately, the general goal of autonomous navigation in complex environments is a problem of general scientific and engineering interest [30], and forest ecologists can expect to benefit from ongoing developments in coming years. We foresee that gradual improvements in below-canopy UAV surveys in forests will make them increasingly useful as a complement to abovecanopy surveys and analyses of satellite data [2], and will lead to more comprehensive assessments of forest physical structure, with benefits for forest ecology and conservation, particularly in evergreen tropical forests.…”

Section: Discussionmentioning

confidence: 99%

“…For below-canopy surveys to be fully autonomous, however, remote human pilots must ultimately be replaced by simultaneous localization and mapping (SLAM) technology, which has advanced rapidly in recent years [26][27][28][29][30]. Liao et al [31] and Gao et al [32] demonstrated navigation of an autonomous UAV in a below-canopy forest environment, but did not focus on measuring forest physical structure.…”

Section: Introductionmentioning

confidence: 99%

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Estimating Tree Diameters from an Autonomous Below-Canopy UAV with Mounted LiDAR

2021

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Below-canopy UAVs hold promise for automated forest surveys because their sensors can provide detailed information on below-canopy forest structures, especially in dense forests, which may be inaccessible to above-canopy UAVs, aircraft, and satellites. We present an end-to-end autonomous system for estimating tree diameters using a below-canopy UAV in parklands. We used simultaneous localization and mapping (SLAM) and LiDAR data produced at flight time as inputs to diameter-estimation algorithms in post-processing. The SLAM path was used for initial compilation of horizontal LiDAR scans into a 2D cross-sectional map, and then optimization algorithms aligned the scans for each tree within the 2D map to achieve a precision suitable for diameter measurement. The algorithms successfully identified 12 objects, 11 of which were trees and one a lamppost. For these, the estimated diameters from the autonomous survey were highly correlated with manual ground-truthed diameters (R2=0.92, root mean squared error = 30.6%, bias = 18.4%). Autonomous measurement was most effective for larger trees (>300 mm diameter) within 10 m of the UAV flight path, for medium trees (200–300 mm diameter) within 5 m, and for trees with regular cross sections. We conclude that fully automated below-canopy forest surveys are a promising, but still nascent, technology and suggest directions for future research.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Estimating Tree Diameters from an Autonomous Below-Canopy UAV with Mounted LiDAR

2021

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“…Figures show that 88% of the UK population owned a smartphone in 2019 [24], rising to 93% and 94%, respectively, in the 18-24 and 25-34 age groups. Therefore, an MR instructional application was designed using web-based technologies and lightweight visual tracking algorithms (as opposed to robust but computationally expensive techniques, such as SLAM [25]), so that it could be accessed even on a budget smartphone, so long as users had access to a camera and reasonably modern Web browser. By utilizing mobile-based AR, setup costs are minimal, as smartphones are almost ubiquitous and not prohibitively expensive for training institutions to supply if necessary.…”

Section: Methodsmentioning

confidence: 99%

Enhancing Vocational Training in the Post-COVID Era through Mobile Mixed Reality

et al. 2021

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COVID-19 and the resulting restrictions have had a massive impact on engineering education, particularly vocational and practical aspects of training. In this study, we present a novel mixed reality (MR) tool to simulate and guide learners through a simple fault diagnosis task of a three-phase power supply. The tool was created as a web-based application that could be accessed from budget smartphones in order to cover the majority of users. Comparisons were made between novices using MR guidance and those with more experience in the task who did not have additional guidance, finding that the novices outperformed the experts across all metrics measured. This indicates that MR could be a valuable tool to supplement traditional vocational learning methods, particularly at a time when physical access to equipment and facilities is scarce. MR has applications across the engineering industry, but the target task of a three-phase power supply was chosen as it has particular relevance to the offshore wind industry, which faces a shortage of skilled engineers and technicians in the coming years.

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“…If a mobile robot can solve the SLAM problem, that robot can move independently in the environment and perform tasks. SLAM is the first problem to be solved for localization, mapping and path planning tasks required for autonomous movement [99]. SLAM simultaneously performs the process of creating an environment map consisting of different properties (markers, obstacles, etc.)…”

Section: Solution Proposal For Uav Applications In Greenhousesmentioning

confidence: 99%

A Comprehensive Survey of the Recent Studies with UAV for Precision Agriculture in Open Fields and Greenhouses

et al. 2022

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The increasing world population makes it necessary to fight challenges such as climate change and to realize production efficiently and quickly. However, the minimum cost, maximum income, environmental pollution protection and the ability to save water and energy are all factors that should be taken into account in this process. The use of information and communication technologies (ICTs) in agriculture to meet all of these criteria serves the purpose of precision agriculture. As unmanned aerial vehicles (UAVs) can easily obtain real-time data, they have a great potential to address and optimize solutions to the problems faced by agriculture. Despite some limitations, such as the battery, load, weather conditions, etc., UAVs will be used frequently in agriculture in the future because of the valuable data that they obtain and their efficient applications. According to the known literature, UAVs have been carrying out tasks such as spraying, monitoring, yield estimation, weed detection, etc. In recent years, articles related to agricultural UAVs have been presented in journals with high impact factors. Most precision agriculture applications with UAVs occur in outdoor environments where GPS access is available, which provides more reliable control of the UAV in both manual and autonomous flights. On the other hand, there are almost no UAV-based applications in greenhouses where all-season crop production is available. This paper emphasizes this deficiency and provides a comprehensive review of the use of UAVs for agricultural tasks and highlights the importance of simultaneous localization and mapping (SLAM) for a UAV solution in the greenhouse.

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SLAM; definition and evolution

Cited by 87 publications

References 96 publications

Estimating Tree Diameters from an Autonomous Below-Canopy UAV with Mounted LiDAR

Estimating Tree Diameters from an Autonomous Below-Canopy UAV with Mounted LiDAR

Enhancing Vocational Training in the Post-COVID Era through Mobile Mixed Reality

A Comprehensive Survey of the Recent Studies with UAV for Precision Agriculture in Open Fields and Greenhouses

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