Aerial Data Collection and Analysis, and Automated Ground Intervention for Precision Farming

Livrables

This deliverable consists of a working prototype for the weed detection and tracking software running on the Bonirob platform with live datastreams

Data Analysis and Interpretation

A software module for converting the raw data gathered by the UAV sensors into tested reliablevegetation indices and plant growth and vitality indicators A validation of the indices calculated using the UAV sensors with ground truth from the FIP Correlations between vegetation indices sensor data and farm inputs along with derivation methodologyThe initial version in M15 will work with the maps delivered in D22 at M15 The final version will work with the updated maps delivered in M35

Development Repository

A webbased repository for the software developments with access for all partner will be setup It will furthermore support bug tracking and projectspecific wiki pages

Dissemination and Exploitation Report

A report about achieved dissemination activities and the exploitation activities

Long-term Navigation Optimization Module

A module that will use the experience from previous operations in the same field to predict likely conditionsin particular possible slippage or ground softness If possible and when enough data will be available this prediction could account for the weather reports and forecast The performance of the terrain condition prediction will be assessed by integrating it in the control and planning framework and observing if and how it makes a difference

Hardware and Software Specification

This deliverable will consist of the software specification of the new components in Flourish and thosecomponents stemming from the existing UAV and UGV platforms that need to be adaptedA second part of the deliverable will consist of the hardware modifications of the platforms includingadaptation of the existing BoniRob platform to meet the precise ground intervention requirements aswell as sensors and hardware modification needed for the aerial survey task

Integrated Perception and Weed Treatment

This deliverable consist of a demonstration of the integration of weed detection and tracking with both treatment modules the mechanical weed treatment module and and the selective spraying module

Initial Data Acquisition

This deliverable will consist of log files recorded from the initial field tests

Use-Case Analysis and Requirements

This deliverable will consist of the usecase specification of the new to develop components in Flourish and the mission scenarios that will be carried out during the field tests

Integrated System Ready and Components Available

A report about the conducted integration activities problems and resolutions as well as a summary of the integration weeks and related activities

Local Collision Avoidance

A software module that computes a local obstacle map and systemcompliant motions that are safe collision free and closely follow the mission path delivered by the module from Task 31

Coordinated UGV and UAV Operations

This deliverable will provide the framework for coordinated operation of the UGV and UAV operationsmaking sure they successfully achieve their individual missions while being able to rendezvous or synchronizewhen required

Second Periodic Report

2nd periodic report for the project

UGV Environment Modeling

The deliverable consists of a report about the UGV mapping system The source code for the optimisation system will be released as open source software and will be shared among the partners

Data Management Plan

This document will outline the projects data management plan It will describe the types of data generated by the project what standards will be used when publishing the data how the data will be made available to third parties and how the data will be preserved during and beyond the lifetime of the project

Integration Plan

Integration plan showing how the new to develop components will integrate among each other and with the existing modules

Third Periodic Report

Third periodic report for the project

End-User Evaluation Report

A report on the evaluation of the system conducted by the end user This report should drive future system specification

Database and User Interface Development

This deliverable will consist of the development of the database and the user interface Database deliverablesinclude a database backend and a visual web fronted that visualize key metrics as specified Userinterface deliverables include a functional fronted that communicates with the robotics system throughthe specified API

Hardware Setup and Modification

This deliverable will consist of hardware modification to the UAV and UGV platforms The modificationsinclude changes specified in D13

UAV Localization

A UAV localization module which gives robust and accurate estimates of the UAV position and orientationon the field using a combination of the onboard sensor data and communication with the UGV

Weed Treatment Modules

This deliverable consists of a working prototype for selective spraying module and the mechanical weed treatment module integrated on the Bonirob platform

UAV Environment Modeling

A software module for combining the gathered sensor data with the output of the localization module into timestamped geolocated maps which are dynamically updated with every UAV missionThe final version will be delivered in M25

Cooperative Environment Modeling

This deliverable will provide the results of joint UAVUGV environment modeling

Navigation Interface

This deliverable will consist of the interface specification for pushing navigation commands to the UGV

Planner Module(s)

A module that publishes a planned trajectory as a list of xyangle poses and a module that creates motion commands for the robot to drive along these waypoints The two modules might also be combined intoone

Adaptive Mission Planning

A software module that takes coverage history and areas of interest as inputs from WP2 and plans a sequence of UAV missions taking into account the current battery status the current map

Model Update After Local Treatment

This deliverable consist of a working prototype for the spraying application

First Periodic Report

First periodic report for the project

Database and User Interface Specification

The deliverable will consist of the software specification for the database 71 Key metrics and interaction requirements must be specified at this time Additionally the key user interactions APItopics for communication and required features of the user interface 74 should be specified

Traversability Analysis Module

A module that publishes a local map with annotations if a cell is traversable by the UGV or not accordingto its specifications

UGV-based crop and weed detection

The deliverable consists of a report about the crop and weed detection system including the evaluationIn addition to that the source code of the system will be shared among the partners The intermediate deliverable at M15 will have limited functionality

Localization Module

A UGV localization module which publishes both global poses onthe field or in a GPS frame and precise local poses regarding plantsrows on the field

Integration Evaluation Report

A report on the conducted evaluation of the system and its performance as well as a summary of the integration and testing weeks and related activities

Project web-page

The website including a public document database will be operational from month 2 on It will be updated continuously during the project

Press Video

A press video explaining the aims of the project the scientific results and the impact to society in plain language

Brochure, Newsletter, and Knowledge Management Report

A nicely laid out brochure at M30 to inform the public about the project and its main objectives and content Public newsletters will be send biannually from M24 on to inform the public about the Flourish project A knowledge management report will be provided to keep internal and external experts informed about new knowledge or patents created in the project

Publications

Beyond point clouds - 3D mapping and field parameter measurements using UAVs

Auteurs: Raghav Khanna, Martin Moller, Johannes Pfeifer, Frank Liebisch, Achim Walter, Roland Siegwart
Publié dans: 2015 IEEE 20th Conference on Emerging Technologies & Factory Automation (ETFA), Numéro 2015, 2015, Page(s) 1-4, ISBN 978-1-4673-7929-8
Éditeur: IEEE
DOI: 10.1109/ETFA.2015.7301583

Studying Phenotypic Variability in Crops using a Hand-held Sensor Platform.

Auteurs: Raghav Khanna, Joern Rehder, Martin Möller, Enric Galceran and Roland Siegwart
Publié dans: Proceedings of the IROS Workshop on Agri-Food Robotics, Numéro 2015, 2015
Éditeur: IROS

Online Informative Path Planning for Active Classification on UAVs

Auteurs: Marija Popovic, Gregory Hitz, Juan Nieto, Roland Siegwart, Enric Galceran
Publié dans: ational Conference on Robotics and Automation (ICRA), Numéro 2016, 2016
Éditeur: IEEE

Fast and Accurate Crop and Weed Identification with Summarized Train Sets for Precision Agriculture

Auteurs: Ciro Potena, Daniele Nardi and Alberto Pretto
Publié dans: Proceedings of the 14th International Conference on Intelligent Autonomous Systems, Numéro 2016, 2016
Éditeur: IAS

Towards automatic UAV data interpretation for precision farming

Auteurs: Johannes Pfeifer, Raghav Khanna, Dragos Constantin, Marija Popovic, Enric Galceran, Norbert Kirchgessner, Achim Walter, Roland Siegwart, Frank Liebisch
Publié dans: International Conference of Agricultural Engineering 2016, Numéro 2016, 2016
Éditeur: International Conference of Agricultural Engineering 2016

Flourish – A robotic approach for automation in crop management

Auteurs: Frank Liebisch, Johannes Pfeifer, Raghav Khanna, Philipp Lottes, Cyrill Stachniss, Tillmann Falck, Slawomir Sander, Roland Siegwart, Achim Walter Enric Galceran
Publié dans: 22. Workshop Computer-Bildanalyse und Unbemannte autonom fliegende Systeme in der Landwirtschaft, Numéro 2016, 2016
Éditeur: Workshop Computer-Bildanalyse und Unbemannte autonom fliegende Systeme in der Landwirtschaft

Fast and effective online pose estimation and mapping for UAVs

Auteurs: Johannes Schneider, Christian Eling, Lasse Klingbeil, Heiner Kuhlmann, Wolfgang Forstner, Cyrill Stachniss
Publié dans: 2016 IEEE International Conference on Robotics and Automation (ICRA), Numéro 2016, 2016, Page(s) 4784-4791, ISBN 978-1-4673-8026-3
Éditeur: IEEE
DOI: 10.1109/ICRA.2016.7487682

An effective classification system for separating sugar beets and weeds for precision farming applications

Auteurs: P. Lottes, M. Hoeferlin, S. Sander, M. Muter, P. Schulze, Lammers C. Stachniss
Publié dans: 2016 IEEE International Conference on Robotics and Automation (ICRA), Numéro 2016, 2016, Page(s) 5157-5163, ISBN 978-1-4673-8026-3
Éditeur: IEEE
DOI: 10.1109/ICRA.2016.7487720

Joint Stem Detection and Crop-Weed Classification for Plant-specific Treatment in Precision Farming

Auteurs: Lottes, Philipp; Behley, Jens; Chebrolu, Nived; Milioto, Andres; Stachniss, Cyrill
Publié dans: International Conference on Intelligent Robots and Systems (IROS), Numéro 1, 2018
Éditeur: IEEE

Real-time Semantic Segmentation of Crop and Weed for Precision Agriculture Robots Leveraging Background Knowledge in CNNs

Auteurs: Milioto, Andres; Lottes, Philipp; Stachniss, Cyrill
Publié dans: International Conference on Robotics and automation (ICRA), Numéro 11, 2018
Éditeur: IEEE

Deep Auxiliary Learning for Visual Localization and Odometry

Auteurs: Valada, Abhinav; Radwan, Noha; Burgard, Wolfram
Publié dans: International conference on Robotics and Automation (ICRA), Numéro 3, 2018
Éditeur: IEEE

Multi-agent Time-based Decision-making for the Search and Action Problem

Auteurs: Miki, Takahiro; Popovic, Marija; Gawel, Abel; Hitz, Gregory; Siegwart, Roland
Publié dans: International Conference on Robotics and Automation (ICRA), Numéro 4, 2018
Éditeur: IEEE

Bonnet: An Open-Source Training and Deployment Framework for Semantic Segmentation in Robotics using CNNs

Auteurs: Milioto, Andres; Stachniss, Cyrill
Publié dans: International Conference on Robotics and Automation (ICRA), Numéro 4, 2018
Éditeur: IEEE

Non-linear model predictive control with adaptive time-mesh refinement

Auteurs: Ciro Potena, Bartolomeo Della Corte, Daniele Nardi, Giorgio Grisetti, Alberto Pretto
Publié dans: 2018 IEEE International Conference on Simulation, Modeling, and Programming for Autonomous Robots (SIMPAR), 2018, Page(s) 74-80, ISBN 978-1-5386-5974-8
Éditeur: IEEE
DOI: 10.1109/SIMPAR.2018.8376274

Design of an Autonomous Racecar: Perception, State Estimation and System Integration

Auteurs: Valls, Miguel de la Iglesia; Hendrikx, Hubertus Franciscus Cornelis; Reijgwart, Victor; Meier, Fabio Vito; Sa, Inkyu; Dubé, Renaud; Gawel, Abel Roman; Bürki, Mathias; Siegwart, Roland
Publié dans: International Conference on Robotics and Automation (ICRA), Numéro 1, 2018
Éditeur: IEEE

The ETH-MAV Team in the MBZ International Robotics Challenge

Auteurs: Bähnemann, Rik; Pantic, Michael; Popvić, Marija; Schindler, Dominik; Tranzatto, Marco; Kamel, Mina; Grimm, Marius; Widauer, Jakob; Siegwart, Roland; Nieto, Juan
Publié dans: arXiv, Numéro 1, 2018
Éditeur: Cornell University

Flourish-A robotic approach for automation in crop management

Auteurs: Achim Walter, Raghav Khanna, Philipp Lottes, Cyrill Stachnis, Roland Siegwart, Juan Nieto, Frank Liebisch
Publié dans: International Conference on Precision Agriculture, Numéro 06, 2018
Éditeur: The International Society of Precision Agriculture

Investigation of ground based and airborne spectral information for Nitrogen fertilizer application optimization in sugar beet

Auteurs: Corinne Müller-Ruh, Frank Liebisch, Johannes Pfeifer, Achim Walter
Publié dans: Bornimer Agrartechnische Berichte, Numéro 90, 2017, ISSN 0947-7314
Éditeur: Leibniz-Institut für Agrartechnik

La robotica autonoma al servizio dell'agricoltura di precisione: primi risultati di classificazione automatica delle infestanti nel progetto Flourish

Auteurs: Ciro Potena, Marco Imperoli, Alberto Pretto, Daniele Nardi, Simona Talevi and Sandro Nardi
Publié dans: Giornate Fitopatologiche, Numéro 2016, 2016, Page(s) 641-650
Éditeur: Fitogest

D2CO: Fast and Robust Registration of 3D Textureless Objects using the Directional Chamfer Distance

Auteurs: Marco Imperoli, Alberto Pretto
Publié dans: D2CO: Fast and Robust Registration of 3D Textureless Objects using the Directional Chamfer Distance, 2015, Page(s) 316-328
Éditeur: Springer International Publishing
DOI: 10.1007/978-3-319-20904-3_29

Mapping and Localization using Multispectral Imaging of the Soil.

Auteurs: Stefan Glaser, Alexander Schaefer and Wolfram Burgard
Publié dans: International Conference on Intelligent Robots and Systems (IROS) Workshop, Unconventional Sensing and Processing for Robotic Visual Perception, 2018
Éditeur: IEEE

Multiresolution Mapping and Informative Path Planning for UAV-based Terrain Monitoring

Auteurs: Marija Popović, Teresa Vidal-Calleja, Gregory Hitz, Inkyu Sa, Roland Siegwart, and Juan Nieto
Publié dans: IEEE Int. Conf. on Intel. Robots & Systems (IROS) 2017, Numéro 2017, 2017
Éditeur: IEEE

On field radiometric calibration for multispectral cameras

Auteurs: Raghav Khanna, Inkyu Sa, Juan Nieto, Roland Siegwart
Publié dans: 2017 IEEE International Conference on Robotics and Automation (ICRA), Numéro 2017, 2017, Page(s) 6503-6509, ISBN 978-1-5090-4633-1
Éditeur: IEEE
DOI: 10.1109/ICRA.2017.7989768

UAV-based crop and weed classification for smart farming

Auteurs: Philipp Lottes, Raghav Khanna, Johannes Pfeifer, Roland Siegwart, Cyrill Stachniss
Publié dans: 2017 IEEE International Conference on Robotics and Automation (ICRA), Numéro 2017, 2017, Page(s) 3024-3031, ISBN 978-1-5090-4633-1
Éditeur: IEEE
DOI: 10.1109/ICRA.2017.7989347

Only Look Once, Mining Distinctive Landmarks from ConvNet for Visual Place Recognition

Auteurs: Zetao Chen, Fabiola Maffra, Inkyu Sa, Margarita Chli
Publié dans: IEEE Int. Conf. on Intel. Robots & Systems (IROS) 2017, Numéro 2017, 2017
Éditeur: IEEE

Efficient path planning for mobile robots with adjustable wheel positions

Auteurs: Freya Fleckenstein, Christian Dornhege, Wolfram Burgard
Publié dans: 2017 IEEE International Conference on Robotics and Automation (ICRA), Numéro 2017, 2017, Page(s) 2454-2460, ISBN 978-1-5090-4633-1
Éditeur: IEEE
DOI: 10.1109/ICRA.2017.7989286

Dynamic System Identification, and Control for a cost effective open-source VTOL MAV

Auteurs: Inkyu Sa, Mina Kamel, Raghav Khanna, Marija Popović, Juan Nieto, Roland Siegwart
Publié dans: Proceedings of the Field of Service Robotics (FSR), Numéro 2017, 2017, ISSN 1610-7438
Éditeur: Springer Verlag

Automatic Model Based Dataset Generation for Fast and Accurate Crop and Weeds Detection

Auteurs: Maurilio Di Cicco, Ciro Potena, Giorgio Grisetti, and Alberto Pretto
Publié dans: IEEE Int. Conf. on Intel. Robots & Systems (IROS) 2017, Numéro 2017, 2017
Éditeur: IEEE

Real-time blob-wise sugar beets vs weeds classification for monitoring fields based on Convolutional Neural Networks

Auteurs: Andres Milioto, Philipp Lottes, and Cyrill Stachniss
Publié dans: ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Numéro 2017, 2017
Éditeur: ISPRS

Effective Target Aware Visual Navigation for UAVs

Auteurs: Ciro Potena, Daniele Nardi and Alberto Pretto
Publié dans: European Conference on Mobile Robots (ECMR) 2017, Numéro 2017, 2017
Éditeur: ECMR

Semi-Supervised Online Visual Crop and Weed Classification in Precision Farming Exploiting Plant Arrangement

Auteurs: Philipp Lottes and Cyrill Stachniss
Publié dans: Numéro 2017, 2017
Éditeur: IEEE

Field Coverage and Weed Mapping by UAV Swarms

Auteurs: Dario Albani, Daniele Nardi, and Trianni Vito
Publié dans: Numéro 2017, 2017
Éditeur: IEEE

A Low-Cost System for High-Rate, High-Accuracy Temporal Calibration for LIDARs and Cameras

Auteurs: Hannes Sommer, Raghav Khanna, Igor Gilitschenski, Zachary Taylor, Roland Siegwart, and Juan Nieto
Publié dans: IEEE Int. Conf. on Intel. Robots & Systems (IROS) 2017, Numéro 2017, 2017
Éditeur: IEEE

Closed-Form Full Map Posteriors for Robot Localization with Lidar Sensors

Auteurs: Lukas Luft, Alexander Schaefer, Tobias Schubert and Wolfram Burgard
Publié dans: International Conference on Intelligent Robots and Systems (IROS), 2017, Numéro 2017, 2017
Éditeur: IEEE

From Plants to Landmarks: Time-invariant Plant Localization that uses Deep Pose Regression in Agricultural Fields

Auteurs: Florian Kraemer, Alexander Schaefer, Andreas Eitel, Johan Vertens, Wolfram Burgard
Publié dans: IEEE Int. Conf. on Intel. Robots & Systems (IROS) 2017, Numéro 2017, 2017
Éditeur: IEEE

On the Accuracy of Dense Fisheye Stereo

Auteurs: Johannes Schneider, Cyrill Stachniss, Wolfgang Forstner
Publié dans: IEEE Robotics and Automation Letters, Numéro 1/1, 2016, Page(s) 227-234, ISSN 2377-3766
Éditeur: IEEE
DOI: 10.1109/LRA.2016.2516509

Effective Vision-based Classification for Separating Sugar Beets and Weeds for Precision Farming

Auteurs: Philipp Lottes, Markus Hörferlin, Slawomir Sander, Cyrill Stachniss
Publié dans: Journal of Field Robotics, Numéro 2016, 2016, ISSN 1556-4959
Éditeur: John Wiley & Sons Ltd.
DOI: 10.1002/rob.21675

weedNet: Dense Semantic Weed Classification Using Multispectral Images and MAV for Smart Farming

Auteurs: Inkyu Sa, Zetao Chen, Marija Popovic, Raghav Khanna, Frank Liebisch, Juan Nieto, Roland Siegwart
Publié dans: IEEE Robotics and Automation Letters, Numéro 3/1, 2018, Page(s) 588-595, ISSN 2377-3766
Éditeur: IEEE
DOI: 10.1109/lra.2017.2774979

Fully Convolutional Networks With Sequential Information for Robust Crop and Weed Detection in Precision Farming

Auteurs: Philipp Lottes, Jens Behley, Andres Milioto, Cyrill Stachniss
Publié dans: IEEE Robotics and Automation Letters, Numéro 3/4, 2018, Page(s) 2870-2877, ISSN 2377-3766
Éditeur: IEEE
DOI: 10.1109/LRA.2018.2846289

WeedMap: A Large-Scale Semantic Weed Mapping Framework Using Aerial Multispectral Imaging and Deep Neural Network for Precision Farming

Auteurs: Inkyu Sa, Marija Popović, Raghav Khanna, Zetao Chen, Philipp Lottes, Frank Liebisch, Juan Nieto, Cyrill Stachniss, Achim Walter, Roland Siegwart
Publié dans: Remote Sensing, Numéro 10/9, 2018, Page(s) 1423, ISSN 2072-4292
Éditeur: Multidisciplinary Digital Publishing Institute (MDPI)
DOI: 10.3390/rs10091423

An Effective Multi-Cue Positioning System for Agricultural Robotics

Auteurs: Marco Imperoli, Ciro Potena, Daniele Nardi, Giorgio Grisetti, Alberto Pretto
Publié dans: IEEE Robotics and Automation Letters, Numéro 3/4, 2018, Page(s) 3685-3692, ISSN 2377-3766
Éditeur: IEEE
DOI: 10.1109/LRA.2018.2855052

Build Your Own Visual-Inertial Drone: A Cost-Effective and Open-Source Autonomous Drone

Auteurs: Inkyu Sa, Mina Kamel, Michael Burri, Michael Bloesch, Raghav Khanna, Marija Popovic, Juan Nieto, Roland Siegwart
Publié dans: IEEE Robotics & Automation Magazine, Numéro 25/1, 2018, Page(s) 89-103, ISSN 1070-9932
Éditeur: Institute of Electrical and Electronics Engineers
DOI: 10.1109/MRA.2017.2771326

Detecting Changes in the Environment Based on Full Posterior Distributions Over Real-Valued Grid Maps

Auteurs: Lukas Luft, Alexander Schaefer, Tobias Schubert, Wolfram Burgard
Publié dans: IEEE Robotics and Automation Letters, Numéro 3/2, 2018, Page(s) 1299-1305, ISSN 2377-3766
Éditeur: IEEE
DOI: 10.1109/LRA.2018.2797317

DCT Maps: Compact Differentiable Lidar Maps Based on the Cosine Transform

Auteurs: Alexander Schaefer, Lukas Luft, Wolfram Burgard
Publié dans: IEEE Robotics and Automation Letters, Numéro 3/2, 2018, Page(s) 1002-1009, ISSN 2377-3766
Éditeur: IEEE
DOI: 10.1109/LRA.2018.2794602

Crop Row Detection on Tiny Plants With the Pattern Hough Transform

Auteurs: Wera Winterhalter, Freya Veronika Fleckenstein, Christian Dornhege, Wolfram Burgard
Publié dans: IEEE Robotics and Automation Letters, Numéro 3/4, 2018, Page(s) 3394-3401, ISSN 2377-3766
Éditeur: IEEE
DOI: 10.1109/LRA.2018.2852841

Safe Local Exploration for Replanning in Cluttered Unknown Environments for Microaerial Vehicles

Auteurs: Helen Oleynikova, Zachary Taylor, Roland Siegwart, Juan Nieto
Publié dans: IEEE Robotics and Automation Letters, Numéro 3/3, 2018, Page(s) 1474-1481, ISSN 2377-3766
Éditeur: IEEE
DOI: 10.1109/LRA.2018.2800109

Learning Context Flexible Attention Model for Long-Term Visual Place Recognition

Auteurs: Zetao Chen, Lingqiao Liu, Inkyu Sa, Zongyuan Ge, Margarita Chli
Publié dans: IEEE Robotics and Automation Letters, Numéro 3/4, 2018, Page(s) 4015-4022, ISSN 2377-3766
Éditeur: IEEE
DOI: 10.1109/LRA.2018.2859916

Robust Long-Term Registration of UAV Images of Crop Fields for Precision Agriculture

Auteurs: Nived Chebrolu, Thomas Labe, Cyrill Stachniss
Publié dans: IEEE Robotics and Automation Letters, Numéro 3/4, 2018, Page(s) 3097-3104, ISSN 2377-3766
Éditeur: IEEE
DOI: 10.1109/LRA.2018.2849603

Improved Tau-Guidance and Vision-aided Navigation for Robust Autonomous Landing of UAVs

Auteurs: Amedeo Rodi Vetrella, Inkyu Sa, Marija Popovic, Raghav Khanna, Juan Nieto, Giancarmine Fasano, Domenico Accardo and Roland Siegwart
Publié dans: Field and Service Robotics, Numéro 2017, 2017, ISSN 1610-7438
Éditeur: Springer Verlag

An Analytical Lidar Sensor Model Based on Ray Path Information

Auteurs: Alexander Schaefer, Lukas Luft, Wolfram Burgard
Publié dans: IEEE Robotics and Automation Letters, Numéro 2/3, 2017, Page(s) 1405-1412, ISSN 2377-3766
Éditeur: IEEE
DOI: 10.1109/LRA.2017.2669376

Control of a Quadrotor With Reinforcement Learning

Auteurs: Jemin Hwangbo, Inkyu Sa, Roland Siegwart, Marco Hutter
Publié dans: IEEE Robotics and Automation Letters, Numéro 2/4, 2017, Page(s) 2096-2103, ISSN 2377-3766
Éditeur: IEEE
DOI: 10.1109/LRA.2017.2720851

Agricultural robot dataset for plant classification, localization and mapping on sugar beet fields

Auteurs: Nived Chebrolu, Philipp Lottes, Alexander Schaefer, Wera Winterhalter, Wolfram Burgard, Cyrill Stachniss
Publié dans: The International Journal of Robotics Research, Numéro 36/10, 2017, Page(s) 1045-1052, ISSN 0278-3649
Éditeur: SAGE Publications
DOI: 10.1177/0278364917720510

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