Combining Local and Global Viewpoint Planning for Fruit Coverage

Zaenker, Tobias; Lehnert, Christopher; McCool, Chris; Bennewitz, Maren

doi:10.1109/ecmr50962.2021.9568836

Cited by 10 publications

(2 citation statements)

References 31 publications

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“…The use of multiple viewpoints planning for an eye‐in‐hand robotic configuration or drone field monitoring is a widely discussed issue in agri‐robotic vision applications (Barth et al, 2016; Bulanon et al, 2009; Hemming et al, 2014; Kurtser & Edan, 2018a, 2018b; Zaenker et al, 2021, 2020). The discussion often focuses on target visibility due to the high occlusion levels requiring multiple viewpoints to overcome the problem.…”

Section: Literature Overviewmentioning

confidence: 99%

NDT‐6D for color registration in agri‐robotic applications

Gupta,

Lilienthal,

Andreasson

et al. 2023

Journal of Field Robotics

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Registration of point cloud data containing both depth and color information is critical for a variety of applications, including in-field robotic plant manipulation, crop growth modeling, and autonomous navigation. However, current state-ofthe-art registration methods often fail in challenging agricultural field conditions due to factors such as occlusions, plant density, and variable illumination.To address these issues, we propose the NDT-6D registration method, which is a color-based variation of the Normal Distribution Transform (NDT) registration approach for point clouds. Our method computes correspondences between pointclouds using both geometric and color information and minimizes the distance between these correspondences using only the three-dimensional (3D) geometric dimensions. We evaluate the method using the GRAPES3D data set collected with a commercial-grade RGB-D sensor mounted on a mobile platform in a vineyard. Results show that registration methods that only rely on depth information fail to provide quality registration for the tested data set. The proposed color-based variation outperforms state-of-the-art methods with a root mean square error (RMSE) of 1.1-1.6 cm for NDT-6D compared with 1.1-2.3 cm for other color-information-based methods and 1.2-13.7 cm for noncolor-information-based methods. The proposed method is shown to be robust against noises using the TUM RGBD data set by artificially adding noise present in an outdoor scenario. The relative pose error (RPE) increased ~14% for our method compared to an increase of ~75% for the best-performing registration method. The obtained average accuracy suggests that the NDT-6D registration methods can be used for in-field precision agriculture applications, for example, crop detection, size-based maturity estimation, and growth modeling.

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Section: Literature Overviewmentioning

confidence: 99%

NDT‐6D for color registration in agri‐robotic applications

Gupta,

Lilienthal,

Andreasson

et al. 2023

Journal of Field Robotics

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show abstract

“…Unlike KinFu, which requires a known map size, large amounts of memory, and GPU computational power, voxblox can dynamically grow maps as it adds point cloud data from different viewpoints. As voxblox can run on a single CPU core with low memory requirements and still build TSDF maps faster than building occupancy maps, we replaced the 3D occupancy maps in [21] with TSDF maps.…”

Section: Related Workmentioning

confidence: 99%

Fruit Mapping with Shape Completion for Autonomous Crop Monitoring

Marangoz¹,

Zaenker²,

Menon³

et al. 2022

Preprint

Self Cite

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Autonomous crop monitoring is a difficult task due to the complex structure of plants. Occlusions from leaves can make it impossible to obtain complete views about all fruits of, e.g., pepper plants. Therefore, accurately estimating the shape and volume of fruits from partial information is crucial to enable further advanced automation tasks such as yield estimation and automated fruit picking. In this paper, we present an approach for mapping fruits on plants and estimating their shape by matching superellipsoids. Our system segments fruits in images and uses their masks to generate point clouds of the fruits. To combine sequences of acquired point clouds, we utilize a real-time 3D mapping framework and build up a fruit map based on truncated signed distance fields. We cluster fruits from this map and use optimized superellipsoids for matching to obtain accurate shape estimates.In our experiments, we show in various simulated scenarios with a robotic arm equipped with an RGB-D camera that our approach can accurately estimate fruit volumes. Additionally, we provide qualitative results of estimated fruit shapes from data recorded in a commercial glasshouse environment.

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Fruit Mapping with Shape Completion for Autonomous Crop Monitoring

Marangoz

Zaenker

Menon

et al. 2022

2022 IEEE 18th International Conference on Automation Science and Engineering (CASE)

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Although inverse kinematics of serial manipulators is a well studied problem, challenges still exist in finding smooth feasible solutions that are also collision aware. Furthermore, with collaborative and service robots gaining traction, different robotic systems have to work in close proximity. This means that the current inverse kinematics approaches have to not only avoid collisions with themselves but also collisions with other robot arms. Therefore, we present a novel approach to compute inverse kinematics for serial manipulators that take into account different constraints while trying to reach a desired end-effector position and/or orientation that avoids collisions with themselves and other arms. Unlike other constraint based approaches, we neither perform expensive inverse Jacobian computations nor do we require arms with redundant degrees of freedom. Instead, we formulate different constraints as weighted cost functions to be optimized by a non-linear optimization solver. Our approach is superior to the state-of-the-art CollisionIK in terms of collision avoidance in the presence of multiple arms in confined spaces with no detected collisions at all in all the experimental scenarios. When the probability of collision is low, our approach shows better performance at trajectory tracking as well. Additionally, our approach is capable of simultaneous yet decentralized control of multiple arms for trajectory tracking in intersecting workspace without any collisions.

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Combining Local and Global Viewpoint Planning for Fruit Coverage

Cited by 10 publications

References 31 publications

NDT‐6D for color registration in agri‐robotic applications

NDT‐6D for color registration in agri‐robotic applications

Fruit Mapping with Shape Completion for Autonomous Crop Monitoring

Fruit Mapping with Shape Completion for Autonomous Crop Monitoring

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