Discover more about PhenoRob: The PhenoRob paper videos present the most important findings published in high-ranked journals.

Shortcut Hulls: Vertex-restricted Outer Simplifications of Polygons by A. Bonerath et al.

Shortcut Hulls: Vertex-restricted Outer Simplifications of Polygons by A. Bonerath et al.

This short paper trailer video is based on the following publication: A. Bonerath, J. Haunert, J. S. B. Mitchell, and B. Niedermann, “Shortcut Hulls: Vertex-restricted Outer Simplifications of Polygons,” in Proceedings of the 33rd Canadian Conference on Computational Geometry , 2021, pp. 12-23.

Cercospora leaf spot modeling in sugar beet by Ispizua, Barreto, Günder, Bauckhage & Mahlein

Cercospora leaf spot modeling in sugar beet by Ispizua, Barreto, Günder, Bauckhage & Mahlein

This short paper trailer video is based on the following publication: F. R. Ispizua Yamati, A. Barreto, A. Günder, C. Bauckhage, and A. -K. Mahlein, “Sensing the occurrence and dynamics of Cercospora leaf spot disease using UAV-supported image data and deep learning,” Sugar Industry, vol. 147, iss. 2, pp. 79-86, 2022. Find more info here: https://sugarindustry.info/paper/28345/ https://www.researchgate.net/publication/358243320_Sensing_the_occurrence_and_dynamics_of_Cercospora_leaf_spot_disease_using_UAV-supported_image_data_and_deep_learning

LatticeNet: fast spatio-temporal point cloud segmentation using permutohedral lattices (Rosu et al.)

LatticeNet: fast spatio-temporal point cloud segmentation using permutohedral lattices (Rosu et al.)

This short paper trailer video is based on the following publication: R. A. Rosu, P. Schütt, J. Quenzel, and S. Behnke, “LatticeNet: fast spatio-temporal point cloud segmentation using permutohedral lattices,” Autonomous Robots, p. 1-16, 2021.

EasyPBR: A Lightweight Physically-Based Renderer

EasyPBR: A Lightweight Physically-Based Renderer

Presentation for paper by Radu Alexandru Rosu and Sven Behnke: “EasyPBR: A Lightweight Physically-Based Renderer” 16th International Conference on Computer Graphics Theory and Applications (GRAPP), 2021 Modern rendering libraries provide unprecedented realism, producing real-time photorealistic 3D graphics on commodity hardware. Visual fidelity, however, comes at the cost of increased complexity and difficulty of usage, with many rendering parameters requiring a deep understanding of the pipeline. We propose EasyPBR as an alternative rendering library that strikes a balance between ease-of-use and visual quality. EasyPBR consists of a deferred renderer that implements recent state-of-the-art approaches in physically based rendering. It offers an easy-to-use Python and C++ interface that allows high-quality images to be created in only a few lines of code or directly through a graphical user interface. The user can choose between fully controlling the rendering pipeline or letting EasyPBR automatically infer the best parameters based on the current scene composition. The EasyPBR library can help the community to more easily leverage the power of current GPUs to create realistic images. These can then be used as synthetic data for deep learning or for creating animations for academic purposes. http://www.ais.uni-bonn.de/papers/GRAPP_2021_Rosu_EasyPBR.pdf

Plants Control Soil Gas Exchanges Possibly Via Mucilage by A. Haupenthal et al.

Plants Control Soil Gas Exchanges Possibly Via Mucilage by A. Haupenthal et al.

PhenoRob PhD Student Adrian Haupenthal talks about his research within Core Project 3: The Soil-Root-Zone.

RAL-ICRA'22: Joint Plant and Leaf Instance Segmentation on Field-Scale UAV Imagery by Weyler et al.

RAL-ICRA’22: Joint Plant and Leaf Instance Segmentation on Field-Scale UAV Imagery by Weyler et al.

J. Weyler, J. Quakernack, P. Lottes, J. Behley, and C. Stachniss, “Joint Plant and Leaf Instance Segmentation on Field-Scale UAV Imagery,” IEEE Robotics and Automation Letters (RA-L), vol. 7, iss. 2, pp. 3787-3794, 2022. doi:10.1109/LRA.2022.3147462 PDF: https://www.ipb.uni-bonn.de/wp-content/papercite-data/pdf/weyler2022ral.pdf #UniBonn #StachnissLab #robotics

WACV'22: In-Field Phenotyping Based on Crop Leaf and Plant Instance Segmentation by Weyler et al.

WACV’22: In-Field Phenotyping Based on Crop Leaf and Plant Instance Segmentation by Weyler et al.

J. Weyler, F. and Magistri, P. Seitz, J. Behley, and C. Stachniss, “In-Field Phenotyping Based on Crop Leaf and Plant Instance Segmentation,” in Proc. of the Winter Conf. on Applications of Computer Vision (WACV), 2022. PDF: https://www.ipb.uni-bonn.de/wp-content/papercite-data/pdf/weyler2022wacv.pdf #UniBonn #StachnissLab #robotics

ICRA'21: Phenotyping Exploiting Differentiable Rendering with Consistency Loss by Magistri et al.

ICRA’21: Phenotyping Exploiting Differentiable Rendering with Consistency Loss by Magistri et al.

F. Magistri, N. Chebrolu, J. Behley, and C. Stachniss, “Towards In-Field Phenotyping Exploiting Differentiable Rendering with Self-Consistency Loss,” in Proceedings of the IEEE Int. Conf. on Robotics & Automation (ICRA), 2021. Paper: https://www.ipb.uni-bonn.de/wp-content/papercite-data/pdf/magistri2021icra.pdf #UniBonn #StachnissLab #robotics #PhenoRob #neuralnetworks #talk

Johannes Postma: Crop Improvement From Phenotyping Roots - Highlights Reveal Expanding Opportunities

Johannes Postma: Crop Improvement From Phenotyping Roots – Highlights Reveal Expanding Opportunities

Johannes A. Postma is a PhenoRob Member and Researcher at the Institute of Bio- and Geosciences (IBG-2), Forschungszentrum Jülich Saoirse R. Tracy, Kerstin A. Nagel, Johannes A. Postma, Heike Fassbender, Anton Wasson, Michelle Watt (2020), Crop Improvement from Phenotyping Roots: Highlights Reveal Expanding Opportunities, Trends in Plant Science, Volume 25, Issue 1, Pages 105-118 https://doi.org/10.1016/j.tplants.2019.10.015

Uwe Rascher: Measuring and understanding the dynamics of plant photosynthesis across scales...

Uwe Rascher: Measuring and understanding the dynamics of plant photosynthesis across scales…

Measuring and understanding the dynamics of plant photosynthesis across scales – from single plants to satellites Prof. Dr. Uwe Rascher is Principal Investigator at PhenoRob and Professor of Quantitative Physiology of Crops, Institute of Bio- and Geosciences (IBG-2), Forschungszentrum Jülich and Institute of Crop Science and Resource Conservation (INRES), University of Bonn Rascher et. al. (2015) Sun-induced fluorescence – a new probe of photosynthesis: First maps from the imaging spectrometer HyPlant Global Change Biology, 21, 4673-4684 https://doi.org/10.1111/gcb.13017 Siegmann et. al. (2019) The High-Performance Airborne Imaging Spectrometer HyPlant—From Raw Images to Top-of-Canopy Reflectance and Fluorescence Products: Introduction of an Automatized Processing Chain Remote Sensing, 11, article no. 2760 https://doi.org/10.3390/rs11232760

G. Schaaf: ITPK1 is an InsP6/ADP phosphotransferase that controls phosphate signaling in Arabidopsis

G. Schaaf: ITPK1 is an InsP6/ADP phosphotransferase that controls phosphate signaling in Arabidopsis

Prof. Dr. Gabriel Schaaf is Principal Investigator at PhenoRob and Professor and head of the Ecophysiology of Plant Nutrition Group, Institute of Crop Science and Resource Conservation (INRES), University of Bonn. ITPK1 is an InsP6/ADP phosphotransferase that controls phosphate signaling in Arabidopsis EstherRiemer, Danye Qiu, Debabrata Laha, Robert K.Harmel, Philipp Gaugler, Verena Gaugler, Michael Frei, Mohammad-Reza Hajirezaei, Nargis Parvin Laha, Lukas Krusenbaum, Robin Schneider, Adolfo Saiardi, Dorothea Fiedler, Henning J. Jessen, Gabriel Schaaf, Ricardo F. H. Giehl Molecular Plant Volume 14, Issue 11, 1 November 2021, Pages 1864-1880 https://doi.org/10.1016/j.molp.2021.07.011

Lasse Klingbeil: Pheno4D: A spatio-temporal dataset of maize and tomato plant point clouds...

Lasse Klingbeil: Pheno4D: A spatio-temporal dataset of maize and tomato plant point clouds…

Dr. Lasse Klingbeil is Postdoc at the Institute of Geodesy and Geoinformation (IGG), University of Bonn and PhenoRob Member. Pheno4D: A spatio-temporal dataset of maize and tomato plant point clouds for phenotyping and advanced plant analysis D. Schunck, F. Magistri, R. A. Rosu, A. Cornelißen, N. Chebrolu, S. Paulus, J. Léon, S. Behnke, C. Stachniss, H. Kuhlmann, and L. Klingbeil PLOS ONE, vol. 16, iss. 8, pp. 1-18, 2021 Paper: https://doi.org/10.1371/journal.pone.0256340 Data: https://www.ipb.uni-bonn.de/data/pheno4d/

A.-K. Mahlein: Making Deep Neural Networks Right for the Right Scientific Reason...

A.-K. Mahlein: Making Deep Neural Networks Right for the Right Scientific Reason…

Prof. Dr. Anne-Katrin Mahlein is Principal Investigator at PhenoRob and Director of the Institute of Sugarbeet Research (IfZ) at the University of Göttingen. Making deep neural networks right for the right scientific reasons by interacting with their explanations. Schramowski, P., Stammer, W., Teso, S. et al. Nat Mach Intell 2, 476–486 (2020). https://doi.org/10.1038/s42256-020-0212-3

Virtual Temporal Samples for RNNs: applied to semantic segmentation in agriculture

Virtual Temporal Samples for RNNs: applied to semantic segmentation in agriculture

Normally, to train a recurrent neural network (RNN), labeled samples from a video (temporal) sequence are required which is laborious and has stymied work in this direction. By generating virtual temporal samples, we demonstrate that it is possible to train a lightweight RNN to perform semantic segmentation on two challenging agricultural datasets. full text in arxiv: https://arxiv.org/abs/2106.10118 check My GitHub for interesting ROS-based projects: https://github.com/alirezaahmadi

Talk by J. Weyler: Joint Plant Instance Detection and Leaf Count Estimation ... (RAL+ICRA'21)

Talk by J. Weyler: Joint Plant Instance Detection and Leaf Count Estimation … (RAL+ICRA’21)

J. Weyler, A. Milioto, T. Falck, J. Behley, and C. Stachniss, “Joint Plant Instance Detection and Leaf Count Estimation for In-Field Plant Phenotyping,” IEEE Robotics and Automation Letters (RA-L), vol. 6, pp. 3599-3606, 2021. doi:10.1109/LRA.2021.3060712 #UniBonn #StachnissLab #robotics #PhenoRob #neuralnetworks #talk

Talk by N. Chebrolu: Adaptive Robust Kernels for Non-Linear Least Squares Problems (RAL+ICRA'21)

Talk by N. Chebrolu: Adaptive Robust Kernels for Non-Linear Least Squares Problems (RAL+ICRA’21)

N. Chebrolu, T. Läbe, O. Vysotska, J. Behley, and C. Stachniss, “Adaptive Robust Kernels for Non-Linear Least Squares Problems,” IEEE Robotics and Automation Letters (RA-L), vol. 6, pp. 2240-2247, 2021. doi:10.1109/LRA.2021.3061331 https://www.ipb.uni-bonn.de/wp-content/papercite-data/pdf/chebrolu2021ral.pdf #UniBonn #StachnissLab #robotics #autonomouscars #slam #talk

Talk by A. Reinke: Simple But Effective Redundant Odometry for Autonomous Vehicles (ICRA'21)

Talk by A. Reinke: Simple But Effective Redundant Odometry for Autonomous Vehicles (ICRA’21)

A. Reinke, X. Chen, and C. Stachniss, “Simple But Effective Redundant Odometry for Autonomous Vehicles,” in Proceedings of the IEEE Int. Conf. on Robotics & Automation (ICRA), 2021. PDF: https://www.ipb.uni-bonn.de/wp-content/papercite-data/pdf/reinke2021icra.pdf Code: https://github.com/PRBonn/MutiverseOdometry #UniBonn #StachnissLab #robotics #autonomouscars #talk

Talk by X. Chen: Range Image-based LiDAR Localization for Autonomous Vehicles (ICRA'21)

Talk by X. Chen: Range Image-based LiDAR Localization for Autonomous Vehicles (ICRA’21)

X. Chen, I. Vizzo, T. Läbe, J. Behley, and C. Stachniss, “Range Image-based LiDAR Localization for Autonomous Vehicles,” in Proceedings of the IEEE Int. Conf. on Robotics & Automation (ICRA), 2021. Paper: https://www.ipb.uni-bonn.de/wp-content/papercite-data/pdf/chen2021icra.pdf Code: https://github.com/PRBonn/range-mcl #UniBonn #StachnissLab #robotics #autonomouscars #neuralnetworks #talk

Talk by I. Vizzo: Poisson Surface Reconstruction for LiDAR Odometry and Mapping (ICRA'21)

Talk by I. Vizzo: Poisson Surface Reconstruction for LiDAR Odometry and Mapping (ICRA’21)

I. Vizzo, X. Chen, N. Chebrolu, J. Behley, and C. Stachniss, “Poisson Surface Reconstruction for LiDAR Odometry and Mapping,” in Proceedings of the IEEE Int. Conf. on Robotics & Automation (ICRA), 2021. Paper: https://www.ipb.uni-bonn.de/wp-content/papercite-data/pdf/vizzo2021icra.pdf Code: https://github.com/PRBonn/puma #UniBonn #StachnissLab #robotics #autonomouscars #slam #talk

SIGSPATIAL'2020: A Time-Window Data-Structure for Spatial Density Maps (Annika Bonerath)

SIGSPATIAL’2020: A Time-Window Data-Structure for Spatial Density Maps (Annika Bonerath)

Talk by R. Sheikh on Gradient and Log-based Active Learning for Semantic Segmentation... (ICRA'20)

Talk by R. Sheikh on Gradient and Log-based Active Learning for Semantic Segmentation… (ICRA’20)

ICRA 2020 talk about the paper: R. Sheikh, A. Milioto, P. Lottes, C. Stachniss, M. Bennewitz, and T. Schultz, “Gradient and Log-based Active Learning for Semantic Segmentation of Crop and Weed for Agricultural Robots,” in Proceedings of the IEEE Int. Conf. on Robotics & Automation (ICRA), 2020. PDF: https://www.ipb.uni-bonn.de/wp-content/papercite-data/pdf/sheikh2020icra.pdf

Talk by J. Quenzel on Beyond Photometric Consistency: Gradient-based Dissimilarity for VO (ICRA'20)

Talk by J. Quenzel on Beyond Photometric Consistency: Gradient-based Dissimilarity for VO (ICRA’20)

ICRA 2020 talk about the paper: J. Quenzel, R. A. Rosu, T. Laebe, C. Stachniss, and S. Behnke, “Beyond Photometric Consistency: Gradient-based Dissimilarity for Improving Visual Odometry and Stereo Matching,” in Proceedings of the IEEE Int. Conf. on Robotics & Automation (ICRA), 2020. PDF: http://www.ipb.uni-bonn.de/pdfs/quenzel2020icra.pdf

Talk by L. Nardi on Long-Term Robot Navigation in Indoor Environments... (ICRA'20)

Talk by L. Nardi on Long-Term Robot Navigation in Indoor Environments… (ICRA’20)

ICRA 2020 talk about the paper: L. Nardi and C. Stachniss, “Long-Term Robot Navigation in Indoor Environments Estimating Patterns in Traversability Changes,” in Proceedings of the IEEE Int. Conf. on Robotics & Automation (ICRA), 2020. PDF: https://www.ipb.uni-bonn.de/wp-content/papercite-data/pdf/nardi2020icra.pdf Discussion Slack Channel: https://icra20.slack.com/app_redirect?channel=moa08_1

Talk by X. Chen on OverlapNet - Loop Closing for LiDAR-based SLAM (RSS'20)

Talk by X. Chen on OverlapNet – Loop Closing for LiDAR-based SLAM (RSS’20)

Talk for the RSS 2020 paper: X. Chen, T. Läbe, A. Milioto, T. Röhling, O. Vysotska, A. Haag, J. Behley, and C. Stachniss, “OverlapNet: Loop Closing for LiDAR-based SLAM,” in Proceedings of Robotics: Science and Systems (RSS), 2020. Paper: https://www.ipb.uni-bonn.de/wp-content/papercite-data/pdf/chen2020rss.pdf Code available: https://github.com/PRBonn/OverlapNet

DIGICROP'20: Spatio-Temporal Registration of Plant Point Clouds by Chebrolu et al.

DIGICROP’20: Spatio-Temporal Registration of Plant Point Clouds by Chebrolu et al.

IROS'20: Segmentation-Based 4D Registration of Plants Point Clouds for Phenotyping by Magistri et al

IROS’20: Segmentation-Based 4D Registration of Plants Point Clouds for Phenotyping by Magistri et al

F. Magistri, N. Chebrolu, and C. Stachniss, “Segmentation-Based 4D Registration of Plants Point Clouds ,” in Proceedings of the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS), 2020. Paper: https://www.ipb.uni-bonn.de/wp-content/papercite-data/pdf/magistri2020iros.pdf #UniBonn #StachnissLab #robotics #PhenoRob #talk

Talk by D. Gogoll: Unsupervised Domain Adaptation for Transferring Plant Classification...(IROS'20)

Talk by D. Gogoll: Unsupervised Domain Adaptation for Transferring Plant Classification…(IROS’20)

D. Gogoll, P. Lottes, J. Weyler, N. Petrinic, and C. Stachniss, “Unsupervised Domain Adaptation for Transferring Plant Classification Systems to New Field Environments, Crops, and Robots,” in Proceedings of the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS), 2020. PAPER: https://www.ipb.uni-bonn.de/wp-content/papercite-data/pdf/gogoll2020iros.pdf

IROS'20: LiDAR Panoptic Segmentation for Autonomous Driving presented by J. Behley

IROS’20: LiDAR Panoptic Segmentation for Autonomous Driving presented by J. Behley

Trailer video for the paper: A. Milioto, J. Behley, C. McCool, and C. Stachniss, “LiDAR Panoptic Segmentation for Autonomous Driving,” in Proceedings of the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS), 2020. Paper: https://www.ipb.uni-bonn.de/wp-content/papercite-data/pdf/milioto2020iros.pdf

IROS'20: Learning an Overlap-based Observation Model for 3D LiDAR Localization by Chen et al.

IROS’20: Learning an Overlap-based Observation Model for 3D LiDAR Localization by Chen et al.

Trailer Video for the work: X. Chen, T. Läbe, L. Nardi, J. Behley, and C. Stachniss, “Learning an Overlap-based Observation Model for 3D LiDAR Localization,” in Proceedings of the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS), 2020. Paper: https://www.ipb.uni-bonn.de/wp-content/papercite-data/pdf/chen2020iros.pdf Code available!

IROS'20: Domain Transfer for Semantic Segmentation of LiDAR Data using DNNs presented by J. Behley

IROS’20: Domain Transfer for Semantic Segmentation of LiDAR Data using DNNs presented by J. Behley

F. Langer, A. Milioto, A. Haag, J. Behley, and C. Stachniss, “Domain Transfer for Semantic Segmentation of LiDAR Data using Deep Neural Networks,” in Proceedings of the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS), 2020. Paper: https://www.ipb.uni-bonn.de/wp-content/papercite-data/pdf/langer2020iros.pdf

Deep Learning for Non-Invasive Diagnosis of Nutrient Deficiencies in Sugar Beet Using RGB Images

Deep Learning for Non-Invasive Diagnosis of Nutrient Deficiencies in Sugar Beet Using RGB Images

Yi, J.; Krusenbaum, L.; Unger, P.; Hüging, H.; Seidel, S.J.; Schaaf, G.; Gall, J. Deep Learning for Non-Invasive Diagnosis of Nutrient Deficiencies in Sugar Beet Using RGB Images. Sensors 2020, 20, 5893. https://www.mdpi.com/1424-8220/20/20/5893

RSS 2020': LatticeNet: Fast Point Cloud Segmentation Using Permutohedral Lattices

RSS 2020′: LatticeNet: Fast Point Cloud Segmentation Using Permutohedral Lattices

LatticeNet: Fast Point Cloud Segmentation Using Permutohedral Lattices by Radu Alexandru Rosu, Peer Schütt, Jan Quenzel, Sven Behnke Deep convolutional neural networks (CNNs) have shown outstanding performance in the task of semantically segmenting images. However, applying the same methods on 3D data still poses challenges due to the heavy memory requirements and the lack of structured data. Here, we propose LatticeNet, a novel approach for 3D semantic segmentation, which takes as input raw point clouds. A PointNet describes the local geometry which we embed into a sparse permutohedral lattice. The lattice allows for fast convolutions while keeping a low memory footprint. Further, we introduce DeformSlice, a novel learned data-dependent interpolation for projecting lattice features back onto the point cloud. We present results of 3D segmentation on various datasets where our method achieves state-of-the-art performance.

EuroCG'2020: Tight Rectilinear Hulls of Simple Polygons

EuroCG’2020: Tight Rectilinear Hulls of Simple Polygons

Tight Rectilinear Hulls of Simple Polygons by A. Bonerath, J. -H. Haunert and B. Niedermann In Proceedings of the 36th European Workshop on Computational Geometry (EuroCG), 2020.

SIGSPATIAL'2019: Retrieving alpha-Shapes and Schematic Polygonal Approximations for Sets of Points..

SIGSPATIAL’2019: Retrieving alpha-Shapes and Schematic Polygonal Approximations for Sets of Points..

Retrieving alpha-Shapes and Schematic Polygonal Approximations for Sets of Points within Queried Temporal Ranges by A. Bonerath, B. Niedermann und J.-H. Haunert In Proceedings of the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, 2019

ICRA'2020: Visual Servoing-based Navigation for Monitoring Row-Crop Fields

ICRA’2020: Visual Servoing-based Navigation for Monitoring Row-Crop Fields

Visual Servoing-based Navigation for Monitoring Row-Crop Fields by A. Ahmadi et al. In Proc. of the IEEE Intl. Conf. on Robotics & Automation (ICRA) , 2020.

IROS'18: Joint Stem Detection and Crop-Weed Classification for Plant-specific Treatment

IROS’18: Joint Stem Detection and Crop-Weed Classification for Plant-specific Treatment

Trailer for the paper: Joint Stem Detection and Crop-Weed Classification for Plant-specific Treatment in Precision Farming by P. Lottes, J. Behley, N. Chebrolu, A. Milioto, and C. Stachniss, IROS 2018.

RAL'18: FCNs with Sequential Information for Robust Crop and Weed Detection by Lottes et al.

RAL’18: FCNs with Sequential Information for Robust Crop and Weed Detection by Lottes et al.

Trailer for the paper: Fully Convolutional Networks with Sequential Information for Robust Crop and Weed Detection in Precision Farming by P. Lottes, J. Behley, A. Milioto, and C. Stachniss, RAL 2018

RAL-ICRA'19: Effective Visual Place Recognition Using Multi-Sequence Maps by Vysotska & Stachniss

RAL-ICRA’19: Effective Visual Place Recognition Using Multi-Sequence Maps by Vysotska & Stachniss

O. Vysotska and C. Stachniss, “Effective Visual Place Recognition Using Multi-Sequence Maps,” IEEE Robotics and Automation Letters (RA-L) and presentation at ICRA, 2019. PDF: http://www.ipb.uni-bonn.de/wp-content/papercite-data/pdf/vysotska2019ral.pdf

ICRA'19: Actively Improving Robot Navigation On Different Terrains Using GPMMs by Nardi et al.

ICRA’19: Actively Improving Robot Navigation On Different Terrains Using GPMMs by Nardi et al.

L. Nardi and C. Stachniss, “Actively Improving Robot Navigation On Different Terrains Using Gaussian Process Mixture Models,” in Proc. of the IEEE Intl. Conf. on Robotics & Automation (ICRA) , 2019. http://www.ipb.uni-bonn.de/wp-content/papercite-data/pdf/nardi2019icra-airn.pdf

ICRA'19: Robot Localization Based on Aerial Images for Precision Agriculture by Chebrolu et al.

ICRA’19: Robot Localization Based on Aerial Images for Precision Agriculture by Chebrolu et al.

Robot Localization Based on Aerial Images for Precision Agriculture Tasks in Crop Fields by N. Chebrolu, P. Lottes, T. Laebe, and C. Stachniss In Proc. of the IEEE Intl. Conf. on Robotics & Automation (ICRA) , 2019. Paper: http://www.ipb.uni-bonn.de/wp-content/papercite-data/pdf/chebrolu2019icra.pdf