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NDT-6D for color registration in agri-robotic applications
Centre for Applied Autonomous Sensor Systems, Institutionen för naturvetenskap & teknik, Örebro University, Örebro, Sweden.
Centre for Applied Autonomous Sensor Systems, Institutionen för naturvetenskap & teknik, Örebro University, Örebro, Sweden; Perception for Intelligent Systems, Technical University of Munich, Munich, Germany.
Centre for Applied Autonomous Sensor Systems, Institutionen för naturvetenskap & teknik, Örebro University, Örebro, Sweden.
Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics. Centre for Applied Autonomous Sensor Systems, Institutionen för naturvetenskap & teknik, Örebro University, Örebro, Sweden.ORCID iD: 0000-0003-4685-379X
2023 (English)In: Journal of Field Robotics, ISSN 1556-4959, E-ISSN 1556-4967, Vol. 40, no 6, p. 1603-1619Article in journal (Refereed) Published
Abstract [en]

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-of-the-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.

Place, publisher, year, edition, pages
John Wiley & Sons, 2023. Vol. 40, no 6, p. 1603-1619
Keywords [en]
agricultural robotics, color pointcloud, in‐field sensing, machine perception, RGB‐D registration, stereo IR, vineyardJ
National Category
Robotics Computer Vision and Robotics (Autonomous Systems)
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:umu:diva-208383DOI: 10.1002/rob.22194ISI: 000991774400001Scopus ID: 2-s2.0-85159844423OAI: oai:DiVA.org:umu-208383DiVA, id: diva2:1758569
Funder
EU, Horizon 2020Available from: 2023-05-23 Created: 2023-05-23 Last updated: 2023-11-13Bibliographically approved

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Kurtser, Polina

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