A navigation and mapping method for UAS during under-the-canopy forest operations

Schultz, Adam; Gilabert, Russell; Bharadwaj, Akshay; Haag, Maarten Uijt de; Zhu, Zhen

doi:10.1109/plans.2016.7479768

Cited by 7 publications

(3 citation statements)

References 12 publications

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“…These scenarios are usually not chosen for specific applications in these scenarios for SLAM algorithm implementation but only for algorithm evaluation in outdoor open spaces. For other scenarios such as forest, agriculture, industry, aquatic, the selection of these scenarios aimed to address some challenges found in the specific scenarios (Cui et al, 2014; Schultz et al, 2016; Yang, Dani, et al, 2017). However, the number of selected studies worked in forest, agriculture, industry, aquatic is much less than in urban and rural.…”

Section: Discussionmentioning

confidence: 99%

UAV‐based simultaneous localization and mapping in outdoor environments: A systematic scoping review

Wang,

Kooistra,

Pan

et al. 2024

Journal of Field Robotics

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This study aims to investigate the current knowledge of unmanned aerial vehicle (UAV)‐based simultaneous localization and mapping (SLAM) in outdoor environments and to discuss challenges and limitations in this field. A literature search was conducted in three online databases (Web of Science, Scopus, and IEEE) for articles published before October 2022 related to UAV‐based SLAM. A scoping review was carried out to identify the key concepts and applications, and discover research gaps in the use of algorithm‐oriented and task‐oriented, open‐source studies. A total of 97 studies met the criteria after conducting a two‐step screening by a systematic method followed the Preferred Reporting Items for Systematic Reviews and Meta‐Analyses. Among eligible studies, 97 were classified into two main categories: algorithm‐oriented studies and task‐oriented studies. The analysis of the literature revealed that the majority of the studies were focused on the development and implementation of new algorithms and algorithms. This review highlights the significance and diversity of sensors utilized in UAVs in different tasks and applications scenarios that employ different types of sensors. The evaluation method is able to show the real results and performance of the new algorithms in the target scenarios compared with the evaluation method by the public data set and simulation platform.

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Section: Discussionmentioning

confidence: 99%

UAV‐based simultaneous localization and mapping in outdoor environments: A systematic scoping review

Wang,

Kooistra,

Pan

et al. 2024

Journal of Field Robotics

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“…Traditionally, work in the field of forest mapping has been done from stationary or ground-based platforms, using heavy and power-hungry laser-based scanning, and requiring offline processing of the data to form a map (Takashi et al, 2014;Pierzchała et al, 2018). Mobile, online systems often extract only tree trunks from the scene, such as in Schultz et al (2016) or Liao et al (2016), a simplification that has also been used for RGBD systems in Fan et al (2020). This avoids much of the complexity of the forest scene but also limits the usefulness of the final map to only information about placement of trees.…”

Section: Navigating and Mapping Forestsmentioning

confidence: 99%

Lost in the Woods? Place Recognition for Navigation in Difficult Forest Environments

Garforth

Webb

2020

Front. Robot. AI

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Forests present one of the most challenging environments for computer vision due to traits, such as complex texture, rapidly changing lighting, and high dynamicity. Loop closure by place recognition is a crucial part of successfully deploying robotic systems to map forests for the purpose of automating conservation. Modern CNN-based place recognition systems like NetVLAD have reported promising results, but the datasets used to train and test them are primarily of urban scenes. In this paper, we investigate how well NetVLAD generalizes to forest environments and find that it out performs state of the art loop closure approaches. Finally, integrating NetVLAD with ORBSLAM2 and evaluating on a novel forest data set, we find that, although suitable locations for loop closure can be identified, the SLAM system is unable to resolve matched places with feature correspondences. We discuss additional considerations to be addressed in future to deal with this challenging problem.

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“…Wang et al (2021) tested the single-flight integration of above-and under-canopy UAV laser scanning for forest investigation in order to obtain accurate canopy height estimates [25]. Recent developments in under-canopy navigation include Higuti et al [26], Schultz et al [27], Cui et al [28], and Lin and Stol [29]. The development of a miniaturized robotic mobile mapping system for under-canopy phenotyping was recently presented by Manish et al [30].…”

Section: Introductionmentioning

confidence: 99%

Under-Canopy UAV Laser Scanning Providing Canopy Height and Stem Volume Accurately

Hyyppä

Hakala

et al. 2021

Forests

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The automation of forest field reference data collection has been an intensive research objective for laser scanning scientists ever since the invention of terrestrial laser scanning more than two decades ago. In this study, we demonstrated that an under-canopy UAV laser scanning system utilizing a rotating laser scanner can alone provide accurate estimates of canopy height and stem volume for the majority of trees in a boreal forest. We mounted a rotating laser scanner based on a Velodyne VLP-16 sensor onboard a manually piloted UAV. The UAV was commanded with the help of a live video feed from the onboard camera. Since the system was based on a rotating laser scanner providing varying view angles, all important elements such as treetops, branches, trunks, and ground could be recorded with laser hits. In an experiment including two different forest structures, namely sparse and obstructed canopy, we showed that our system can measure the heights of individual trees with a bias of −20 cm and a standard error of 40 cm in the sparse forest and with a bias of −65 cm and a standard error of 1 m in the obstructed forest. The accuracy of the obtained tree height estimates was equivalent to airborne above-canopy UAV surveys conducted in similar forest conditions or even at the same sites. The higher underestimation and higher inaccuracy in the obstructed site can be attributed to three trees with a height exceeding 25 m and the reduced point density of these tree tops due to occlusion and the limited ranging capacity of the scanner. Additionally, we used our system to estimate the stem volumes of individual trees with a standard error at the level of 10%. This level of error is equivalent to the error obtained when merging above-canopy UAV laser scanner data with terrestrial point cloud data. The results show that we do not necessarily need a combination of terrestrial point clouds and point clouds collected using above-canopy UAV systems in order to accurately estimate the heights and the volumes of individual trees in reference data collection.

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A navigation and mapping method for UAS during under-the-canopy forest operations

Cited by 7 publications

References 12 publications

UAV‐based simultaneous localization and mapping in outdoor environments: A systematic scoping review

UAV‐based simultaneous localization and mapping in outdoor environments: A systematic scoping review

Lost in the Woods? Place Recognition for Navigation in Difficult Forest Environments

Under-Canopy UAV Laser Scanning Providing Canopy Height and Stem Volume Accurately

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