Optimized EIF-SLAM algorithm for precision agriculture mapping based on stems detection
Abstract
Precision agricultural maps are required for agricultural machinery navigation, path planning and plantation supervision. In this work we present a Simultaneous Localization and Mapping (SLAM) algorithm solved by an Extended Information Filter (EIF) for agricultural environments (olive groves). The SLAM algorithm is implemented on an unmanned non-holonomic car-like mobile robot. The map of the environment is based on the detection of olive stems from the plantation. The olive stems are acquired by means of both: a range sensor laser and a monocular vision system. A support vector machine (SVM) is implemented on the vision system to detect olive stems on the images acquired from the environment. Also, the SLAM algorithm has an optimization criterion associated with it. This optimization criterion is based on the correction of the SLAM system state vector using only the most meaningful stems – from an estimation convergence perspective – extracted from the environment information without compromising the estimation consistency. The optimization criterion, its demonstration and experimental results within real agricultural environments showing the performance of our proposal are also included in this work.
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Depth estimation, pose estimation, VO/VIO, and SLAM constitute the important 3D perceiving ability of AIoT and could be very useful in smart transportation, smart industry, smart agriculture, smart cities, and smart homes.
“Depth estimation, pose estimation, VO/VIO, and SLAM constitute the important 3D perceiving ability of AIoT, which could be very useful in smart transportation [137], smart industry [138], smart agriculture [139], [140], [141], smart cities and homes [142], [143], [144].”
From Empowering things with intelligence: a survey of the progress, challenges, and opportunities in artificial intelligence of things (2021) · cites this paper as [139]
The path planning and self-localization and navigation abilities of UAVs are critical to complete agricultural tasks, which have been studied in the agricultural scenario.
“The path planning [140] and self-localization and navigation [139] abilities of UAVs are critical to complete the above tasks, which have been studied in the agricultural scenario.”
From Empowering things with intelligence: a survey of the progress, challenges, and opportunities in artificial intelligence of things (2021) · cites this paper as [139]
Cites
Distinctive Image Features from Scale-Invariant Keypoints
Lowe, D. G. (2004) · International Journal of Computer Vision · doi:10.1023/b:visi.0000029664.99615.94 · Cited by 55,589 · see citing works
Histograms of Oriented Gradients for Human Detection
Dalal, N., & Triggs, B. (2005) · 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) · doi:10.1109/cvpr.2005.177 · Cited by 33,584 · see citing works
Neural Networks: A Comprehensive Foundation
Haykin, S. (1998) · Cited by 30,070 · see citing works
Wu, Y., & Vapnik, V. N. (1999) · Technometrics · doi:10.2307/1271368 · Cited by 27,236 · see citing works
Thrun, S. (2002) · CACM · doi:10.1145/504729.504754 · Cited by 7,941 · see citing works
Simultaneous localization and mapping: part I
Durrant-Whyte, H., & Bailey, T. (2006) · IEEE Robotics & Automation Magazine · doi:10.1109/mra.2006.1638022 · Cited by 4,199 · see citing works
An introduction to kernel-based learning algorithms
Müller, K. R., Mika, S., Rätsch, G., Tsuda, K., & Schölkopf, B. (2001) · IEEE Transactions on Neural Networks · doi:10.1109/72.914517 · Cited by 3,485 · see citing works
Flexible camera calibration by viewing a plane from unknown orientations
Zhang, Z. (1999) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.1999.791289 · Cited by 2,690 · see citing works
A solution to the simultaneous localization and map building (SLAM) problem
Dissanayake, M. W. M. G., Newman, P., Clark, S., Durrant-Whyte, H. F., & Csorba, M. (2001) · IEEE Transactions on Robotics and Automation · doi:10.1109/70.938381 · Cited by 2,661 · see citing works
Simultaneous localization and mapping (SLAM): part II
Bailey, T., & Durrant-Whyte, H. (2006) · IEEE Robotics & Automation Magazine · doi:10.1109/mra.2006.1678144 · Cited by 2,554 · see citing works
Scale-space theory: a basic tool for analyzing structures at different scales
Tony Lindeberg, C. (1994) · Journal of Applied Statistics · doi:10.1080/757582976 · Cited by 1,199 · see citing works
Position referencing and consistent world modeling for mobile robots
Chatila, R., & Laumond, J. (1985) · Proceedings. 1985 IEEE International Conference on Robotics and Automation · doi:10.1109/robot.1985.1087373 · Cited by 683 · see citing works
Matrix Algebra From a Statistician's Perspective.
Harville, D. A. (1998) · Biometrics · doi:10.2307/2533884 · Cited by 671 · see citing works
Matrix Algebra From a Statistician's Perspective
Harville, D. A. (1998) · Technometrics · doi:10.1080/00401706.1998.10485214 · Cited by 538 · see citing works
6D SLAM—3D mapping outdoor environments
Nüchter, A., Lingemann, K., Hertzberg, J., & Hartmut Surmann, A. (2007) · Journal of Field Robotics · doi:10.1002/rob.20209 · Cited by 450 · see citing works
Maintaining Representations of the Environment of a Mobile Robot
Ayache, N., & Faugeras, O. D. (1989) · Springer eBooks · doi:10.1007/978-1-4613-8997-2_16 · Cited by 431 · see citing works
Wells, P. N. T. (1994) · Medical Engineering & Physics · doi:10.1016/1350-4533(94)90015-9 · Cited by 319 · see citing works
Using laser range data for 3D SLAM in outdoor environments
Cole, D., & Newman, P. M. (2006) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/robot.2006.1641929 · Cited by 319 · see citing works
Automatic guidance of a four‐wheel‐steering mobile robot for accurate field operations
Cariou, C., Lenain, R., Thuilot, B., & Berducat, M. (2009) · Journal of Field Robotics · doi:10.1002/rob.20282 · Cited by 124 · see citing works
Mobile robot SLAM for line-based environment representation
Garulli, A., Giannitrapani, A., Rossi, A., & Vicino, A. (2006) · IEEE Conference on Decision and Control (CDC) · doi:10.1109/cdc.2005.1582461 · Cited by 121 · see citing works
Real-Time Hierarchical Outdoor SLAM Based on Stereovision and GPS Fusion
Schleicher, D., Bergasa, L. M., Ocaña, M., Barea, R., & López-Guillén, E. (2009) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2009.2026317 · Cited by 103 · see citing works
IEEE Conference on Computer Vision and Pattern Recognition, 17-22 June 2007
Chekhlov, D., Pupilli, M., Mayol-Cuevas, W. W., & Calway, A. D. (2007) · Cited by 80 · see citing works
An autonomous rice transplanter guided by global positioning system and inertial measurement unit
Nagasaka, Y., Saito, H., Tamaki, K., Seki, M., Kobayashi, K., & Taniwaki, K. (2009) · Journal of Field Robotics · doi:10.1002/rob.20294 · Cited by 79 · see citing works
Robust Real-Time Visual SLAM Using Scale Prediction and Exemplar Based Feature Description
Chekhlov, D., Pupilli, M., Mayol-Cuevas, W. W., & Calway, A. D. (2007) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2007.383026 · Cited by 73 · see citing works
Development and implementation of a team of robotic tractors for autonomous peat moss harvesting
Johnson, D. A., Naffin, D. J., Puhalla, J. S., Sanchez, J., & Wellington, C. (2009) · Journal of Field Robotics · doi:10.1002/rob.20297 · Cited by 66 · see citing works
Corn plant sensing using real‐time stereo vision
Jin, J., & Tang, L. X. (2009) · Journal of Field Robotics · doi:10.1002/rob.20293 · Cited by 63 · see citing works
Growing semantically meaningful models for visual SLAM
Flint, A., Christopher Mei, A., Reid, I. D., & Murray, D. W. (2010) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2010.5540176 · Cited by 57 · see citing works
High accuracy navigation using laser range sensors in outdoor applications
Guivant, J., Nebot, E., & Baiker, S. (2002) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/robot.2000.845326 · Cited by 40 · see citing works
Scale Space Histogram of Oriented Gradients for Human Detection
He, N., Cao, J., & Song, L. (2008) · International Symposium on Information Science and Engineering · doi:10.1109/isise.2008.308 · Cited by 39 · see citing works
Adaptive steering control of a farm tractor with varying yaw rate properties
Derrick, J. B., & Bevly, D. M. (2009) · Journal of Field Robotics · doi:10.1002/rob.20291 · Cited by 34 · see citing works
6D SLAM3D mapping outdoor environments
NüchterAndreas, LingemannKai, HertzbergJoachim, & SurmannHartmut (2007) · Journal of Field Robotics · doi:10.5555/1291068.1291071 · Cited by 17 · see citing works
Towards features updating selection based on the covariance matrix of the SLAM system state
Cheein, F. A., di Sciascio, F. A., Scaglia, G. J. E., & Carelli, R. (2010) · Robotica · doi:10.1017/s0263574710000111 · Cited by 11 · see citing works
Self-citationThe hybrid metric map: a solution for precision farming
Masson, F. R., Guivant, J. E., Nieto, J. I., & Nebot, E. (2005) · Latin American Applied Research - An international journal · Cited by 8 · see citing works
Enhancing performance of a trickle bed reactor at low liquid flowrates
Ochoa, J., Castellari, A. T., Cechini, J., & Haure, P. M. (2002) · Americanae (AECID Library) · Cited by 2 · see citing works
Huntsberger, T. L., Keegan, M., & Brizzolara, R. A. (2010) · Journal of Field Robotics · doi:10.1002/rob.20375 · Cited by 2 · see citing works
Flagged first, then by citations
2 of the 37 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
Zhang, J., & Tao, D. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.3039359 · Cited by 774 · see citing works
Human–robot interaction in agriculture: A survey and current challenges
Vásconez, J. P., Kantor, G. A., & Cheein, F. A. (2019) · Biosystems Engineering · doi:10.1016/j.biosystemseng.2018.12.005 · Cited by 319 · see citing works
Agricultural Robotics: Unmanned Robotic Service Units in Agricultural Tasks
Auat Cheein, F. A., & Carelli, R. (2013) · IEEE Industrial Electronics Magazine · doi:10.1109/mie.2013.2252957 · Cited by 231 · see citing works
Self-citationVásconez, J. P., Delpiano, J. F., Vougioukas, S. G., & Cheein, F. A. (2020) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2020.105348 · Cited by 195 · see citing works
Review of Wheeled Mobile Robots’ Navigation Problems and Application Prospects in Agriculture
Gao, X., Li, J., Fan, L., Zhou, Q., Yin, K., Wang, J., Song, C., Huang, L., & Wang, Z. (2018) · IEEE Access · doi:10.1109/access.2018.2868848 · Cited by 176 · see citing works
Development status and trend of agricultural robot technology
Jin, Y., Liu, J., Xu, Z., Yuan, S., Li, P., & Wang, J. (2021) · International journal of agricultural and biological engineering · doi:10.25165/j.ijabe.20211404.6821 · Cited by 166 · see citing works
Localization and Mapping for Robots in Agriculture and Forestry: A Survey
Aguiar, A., dos Santos, F. N., Boaventura‐Cunha, J., Sobreira, H., & Sousa, A. J. (2020) · Robotics · doi:10.3390/robotics9040097 · Cited by 138 · see citing works
Robots in Agriculture: State of Art and Practical Experiences
Roldán, J. J., del Cerro, J., Ramos, D. G., Garcia-Aunon, P., Garzón, M., León, J. D. L. F., & Barrientos, A. (2018) · InTech eBooks · doi:10.5772/intechopen.69874 · Cited by 124 · see citing works
Safety and ergonomics in human-robot interactive agricultural operations
Benos, L. T., Bechar, A., & Bochtis, D. (2020) · Biosystems Engineering · doi:10.1016/j.biosystemseng.2020.09.009 · Cited by 124 · see citing works
Recent developments and applications of simultaneous localization and mapping in agriculture
Ding, H.-Z., Zhang, B.-H., Zhou, J., Yan, Y.-X., Tian, G.-Z., & Gu, B.-X. (2022) · J. Field Robotics · doi:10.1002/rob.22077 · Cited by 114 · see citing works
A Survey of Robotic Harvesting Systems and Enabling Technologies
Δρούκας, Λ., Doulgeri, Z., Tsakiridis, N., Triantafyllou, D., Kleitsiotis, I., Mariolis, I. G., Giakoumis, D., Tzovaras, D. K., Katerıs, D., & Bochtis, D. (2023) · Journal of Intelligent & Robotic Systems · doi:10.1007/s10846-022-01793-z · Cited by 133 · see citing works
An Extensive Review of Mobile Agricultural Robotics for Field Operations: Focus on Cotton Harvesting
Fue, K. G., Porter, W. M., Barnes, E. M., & Rains, G. C. (2020) · AgriEngineering · doi:10.3390/agriengineering2010010 · Cited by 114 · see citing works
Towards autonomous mapping in agriculture: A review of supportive technologies for ground robotics
Fasiolo, D. T., Scalera, L., Maset, E., & Gasparetto, A. (2023) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2023.104514 · Cited by 112 · see citing works
Shalal, N., Low, T., McCarthy, C. L., & Hancock, N. H. (2015) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2015.09.025 · Cited by 104 · see citing works
Design of an Autonomous Precision Pollination Robot
Ohi, N., Lassak, K., Watson, R. M., Strader, J., Du, Y., Yang, C., Hedrick, G., Nguyen, J., Harper, S., Reynolds, D., et al. (2018) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2018.8594444 · Cited by 101 · see citing works
Under canopy light detection and ranging‐based autonomous navigation
Higuti, V. A. H., Velasquez, A. E. B., Magalhães, D. V., Becker, M., & Chowdhary, G. V. (2018) · Journal of Field Robotics · doi:10.1002/rob.21852 · Cited by 99 · see citing works
Advances in Structured Light Sensors Applications in Precision Agriculture and Livestock Farming
Rosell-Polo, J. R., Cheein, F. A., Gregorio, E., Andújar, D., Franquesa, L. P., Masip, J., & Escolà, A. (2015) · Advances in agronomy · doi:10.1016/bs.agron.2015.05.002 · Cited by 88 · see citing works
Large-scale mapping in complex field scenarios using an autonomous car
Mutz, F. W., Veronese, L. D. P., Oliveira-Santos, T., de Aguiar, E., Cheein, F. A., & Souza, A. F. D. (2015) · Expert Systems with Applications · doi:10.1016/j.eswa.2015.10.045 · Cited by 85 · see citing works
Shalal, N., Low, T., McCarthy, C. L., & Hancock, N. H. (2015) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2015.09.026 · Cited by 84 · see citing works
Chen, X., Wang, S., Zhang, B., & Luo, L. (2018) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2018.02.009 · Cited by 83 · see citing works
Vineyard trunk detection using deep learning – An experimental device benchmark
Aguiar, A., dos Santos, F. N., Santos, L. C., Filipe, V. M. D. J., & Sousa, A. J. (2020) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2020.105535 · Cited by 65 · see citing works
Trajectory Tracking Controller Design for Unmanned Vehicles: A New Methodology
Cheein, F. A., & Scaglia, G. J. E. (2013) · Journal of Field Robotics · doi:10.1002/rob.21492 · Cited by 64 · see citing works
ORB-Livox: A real-time dynamic system for fruit detection and localization
Liu, T., Kang, H., & Chen, C. (2023) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2023.107834 · Cited by 60 · see citing works
Li, Y., Li, J., Zhou, W., Yao, Q., Nie, J., & Qi, X. (2022) · Agriculture · doi:10.3390/agriculture12091445 · Cited by 57 · see citing works
Sensing and Artificial Perception for Robots in Precision Forestry: A Survey
Ferreira, J. F., Portugal, D., Andrada, M. E., Machado, P., Rocha, R. P., & Peixoto, P. (2023) · Robotics · doi:10.3390/robotics12050139 · Cited by 58 · see citing works
Towards a Reliable Robot for Steep Slope Vineyards Monitoring
dos Santos, F. N., Sobreira, H., Campos, D., Morais, R., Moreira, A. P., & Contente, O. (2016) · Journal of Intelligent & Robotic Systems · doi:10.1007/s10846-016-0340-5 · Cited by 53 · see citing works
A review of autonomous navigation systems in agricultural environments
Shalal, N., Low, T., McCarthy, C. L., & Hancock, N. (2013) · University of Southern Queensland ePrints (University of Southern Queensland) · Cited by 52 · see citing works
Machine-Vision Systems Selection for Agricultural Vehicles: A Guide
Pájares, G., García-Santillán, I. D., Campos, Y., Montalvo, M., Hernández, J. M. G., Emmi, L., Romeo, J., Guijarro, M., & de Santos, P. G. (2016) · Journal of Imaging · doi:10.3390/jimaging2040034 · Cited by 51 · see citing works
Kinect v2 Sensor-Based Mobile Terrestrial Laser Scanner for Agricultural Outdoor Applications
Rosell-Polo, J. R., Gregorio, E., Gené-Mola, J., Llorens, J., Torrent, X., Arnó, J., & Escolà, A. (2017) · IEEE/ASME Transactions on Mechatronics · doi:10.1109/tmech.2017.2663436 · Cited by 49 · see citing works
AGRI-SLAM: a real-time stereo visual SLAM for agricultural environment
Islam, R., Habibullah, H., & Hossain, T. (2023) · Autonomous Robots · doi:10.1007/s10514-023-10110-y · Cited by 49 · see citing works
GNSS-IMU-assisted colored ICP for UAV-LiDAR point cloud registration of peach trees
Yuan, W., Choi, D. D., & Bolkas, D. (2022) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2022.106966 · Cited by 47 · see citing works
An Effective Multi-Cue Positioning System for Agricultural Robotics
Imperoli, M., Potena, C., Nardi, D., Grisetti, G., & Pretto, A. (2018) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2018.2855052 · Cited by 44 · see citing works
A novel tree trunk detection method for oil-palm plantation navigation
Juman, M. A., Wong, Y. W., Rajkumar, R. K., & Goh, L. J. (2016) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2016.09.002 · Cited by 43 · see citing works
SLAM-based incremental convex hull processing approach for treetop volume estimation
Cheein, F. A., & Guivant, J. E. (2014) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2014.01.002 · Cited by 42 · see citing works
Fue, K. G., Porter, W. M., Barnes, E. M., Li, C., & Rains, G. C. (2020) · Sensors · doi:10.3390/s20164412 · Cited by 39 · see citing works
Simultaneous localisation and mapping in a complex field environment
Lepej, P., & Rakun, J. (2016) · Biosystems Engineering · doi:10.1016/j.biosystemseng.2016.08.004 · Cited by 38 · see citing works
Visual Trunk Detection Using Transfer Learning and a Deep Learning-Based Coprocessor
Aguiar, A., dos Santos, F. N., Sousa, A. J., Oliveira, P. M., & Santos, L. C. (2020) · IEEE Access · doi:10.1109/access.2020.2989052 · Cited by 38 · see citing works
Research on virtual Ackerman steering model based navigation system for tracked vehicles
Zhang, L., Zhang, R., Li, L., Ding, C., Zhang, D., & Chen, L. (2021) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2021.106615 · Cited by 37 · see citing works
Spatio-temporal analysis for obstacle detection in agricultural videos
Campos, Y., Sossa, H., & Pájares, G. (2016) · Applied Soft Computing · doi:10.1016/j.asoc.2016.03.016 · Cited by 36 · see citing works
Trends in Remote Sensing Technologies in Olive Cultivation
Anastasiou, E., Balafoutis, A. Τ., & Fountas, S. (2022) · Smart Agricultural Technology · doi:10.1016/j.atech.2022.100103 · Cited by 35 · see citing works
Mechatronic terrestrial LiDAR for canopy porosity and crown surface estimation
Pfeiffer, S. A., Guevara, J., Cheein, F. A., & Sanz, R. (2018) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2018.01.022 · Cited by 34 · see citing works
Lyu, H., Park, C.-H., Han, D., Kwak, S. W., & Choi, B. (2018) · Symmetry · doi:10.3390/sym10090355 · Cited by 34 · see citing works
UAV‐based simultaneous localization and mapping in outdoor environments: A systematic scoping review
Wang, K., Kooistra, L., Pan, R., Wang, W., & Valente, J. R. P. (2024) · Journal of Field Robotics · doi:10.1002/rob.22325 · Cited by 34 · see citing works
Lidar Scan matching EKF-SLAM using the differential model of vehicle motion
Wang, D., Liang, H., Mei, T., Zhu, H., Fu, J. W., & Tao, X. H. (2013) · IEEE Intelligent Vehicles Symposium · doi:10.1109/ivs.2013.6629582 · Cited by 32 · see citing works
Bringing Semantics to the Vineyard: An Approach on Deep Learning-Based Vine Trunk Detection
Aguiar, A., Monteiro, N. N., dos Santos, F. N., Pires, E. J. S., da Silva, D. Q., Sousa, A. J., & Boaventura‐Cunha, J. (2021) · Agriculture · doi:10.3390/agriculture11020131 · Cited by 26 · see citing works
Localization Based on Natural Features Detector for Steep Slope Vineyards
Mendes, J. M., dos Santos, F. N., Ferraz, N., Couto, P., & Morais, R. (2018) · Journal of Intelligent & Robotic Systems · doi:10.1007/s10846-017-0770-8 · Cited by 26 · see citing works
Monocular Visual-Inertial Odometry for Agricultural Environments
Song, K., Li, J., Qiu, R., & Yang, G. (2022) · IEEE Access · doi:10.1109/access.2022.3209186 · Cited by 25 · see citing works
Vine Trunk Detector for a Reliable Robot Localization System
Mendes, J. M., dos Santos, F. N., Ferraz, N., Couto, P., & Morais, R. (2016) · IEEE International Conference on Autonomous Robot Systems and Competitions · doi:10.1109/icarsc.2016.68 · Cited by 23 · see citing works
Path Planning of Arbitrary Shaped Mobile Robots With Safety Consideration
Zhao, Z., Jin, M., Lu, E., & Yang, S. X. (2021) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2021.3128411 · Cited by 23 · see citing works
Matsuzaki, S., Masuzawa, H., Miura, J., & Oishi, S. (2018) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc.2018.00070 · Cited by 22 · see citing works
Loop Closure Detection and SLAM in Vineyards with Deep Semantic Cues
Papadimitriou, A., Kleitsiotis, I., Kostavelis, I., Mariolis, I. G., Giakoumis, D., Likothanassis, S., & Tzovaras, D. K. (2022) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra46639.2022.9812419 · Cited by 22 · see citing works
Science and Technology Parks: A Futuristic Approach
Makhdoom, I., Lipman, J., Abolhasan, M., & Challen, D. (2022) · IEEE Access · doi:10.1109/access.2022.3159798 · Cited by 22 · see citing works
Optimization methodology to fruit grove mapping in precision agriculture
Giménez, J., Herrera, D., Tosetti, S. R., & Carelli, R. (2015) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2015.06.013 · Cited by 21 · see citing works
Self-citationAn Evaluation of an Apple Canopy Density Mapping System for a Variable-rate Sprayer
Hu, M., & Whitty, M. A. (2019) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2019.12.563 · Cited by 21 · see citing works
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Hroob, I., Polvara, R., Molina, S., Cielniak, G., & Hanheide, M. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-89177-0_17 · Cited by 20 · see citing works
Design of Stickbug: a Six-Armed Precision Pollination Robot
Smith, T., Rijal, M., Tatsch, C., Butts, R. M., Beard, J. J., Cook, R. T., Chu, A., Gross, J. N., & Gu, Y. (2024) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros58592.2024.10801406 · Cited by 18 · see citing works
A preliminary evaluation of vision and laser sensing for tree trunk detection and orchard mapping
Shalal, N., Low, T., McCarthy, C. L., & Hancock, N. H. (2013) · Cited by 17 · see citing works
Teng, P., Zhang, Y., Yamane, T., Kogoshi, M., Yoshida, T., Ota, T., & Nakagawa, J. (2023) · Remote Sensing · doi:10.3390/rs15020408 · Cited by 15 · see citing works
Intelligent Sampling Technique for Path Tracking Controllers
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Giménez, J., Amicarelli, A., Toibero, J. M., di Sciascio, F. A., & Carelli, R. (2018) · Machine Intelligence Research · doi:10.1007/s11633-017-1109-4 · Cited by 14 · see citing works
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Abouzahir, M., Elouardi, A., Bouaziz, S., Latif, R., & Tajer, A. (2014) · World Conference on Complex Systems · doi:10.1109/icocs.2014.7061001 · Cited by 14 · see citing works
Navigation Stack for Robots Working in Steep Slope Vineyard
Santos, L. C., Aguiar, A., dos Santos, F. N., Valente, A. L. G., Boaventura‐Cunha, J., & Sousa, A. J. (2020) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-55180-3_21 · Cited by 14 · see citing works
Research on a Map-Based Cooperative Navigation System for Spraying–Dosing Robot Group
Qin, J., Wang, W., Mao, W., Yuan, M., Liu, H., Ren, Z., Shi, S., & Yang, F. (2022) · Agronomy · doi:10.3390/agronomy12123114 · Cited by 13 · see citing works
Kraemer, F., Schaefer, A., Eitel, A., Vertens, J., & Burgard, W. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1709.04751 · Cited by 13 · see citing works
Cadle-Davidson, L. E., Londo, J. P., Martinez, D., Sapkota, S., & Gutierrez, B. L. (2019) · Compendium of plant genomes · doi:10.1007/978-3-030-18601-2_10 · Cited by 12 · see citing works
A Computational Geometry Approach for Localization and Tracking in GPS-denied Environments*
Veronese, L. D. P., Cheein, F. A., Bastos-Filho, T., Souza, A. F. D., & de Aguiar, E. (2015) · Journal of Field Robotics · doi:10.1002/rob.21594 · Cited by 12 · see citing works
Trunk detection based on laser radar and vision data fusion
Xue, J., Fan, B., Jia, Y. M., Dong, S., & Ding, Q. (2018) · International journal of agricultural and biological engineering · doi:10.25165/j.ijabe.20181106.3725 · Cited by 12 · see citing works
Accurate and Robust Stereo Direct Visual Odometry for Agricultural Environment
Yu, T., Zhou, J., Wang, L., & Xiong, S. (2021) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48506.2021.9561074 · Cited by 11 · see citing works
Parallelization of a Vine Trunk Detection Algorithm For a Real Time Robot Localization System
de Azevedo, F. P., Shinde, P., Santos, L. C., Mendes, J. M., dos Santos, F. N., & Mendonca, H. S. (2019) · IEEE International Conference on Autonomous Robot Systems and Competitions · doi:10.1109/icarsc.2019.8733644 · Cited by 11 · see citing works
Musculoskeletal Risks of Farmers in the Olive Grove (Jaén-Spain)
Alcántara, M. B., Díaz-Pérez, M., Gómez-Galán, M., Pérez-Alonso, J., & Ferre, Á. J. C. (2020) · Agriculture · doi:10.3390/agriculture10110511 · Cited by 10 · see citing works
Smart Precision Weeding in Agriculture Using 5IR Technologies
San, C. T., & Kakani, V. (2025) · Electronics · doi:10.3390/electronics14132517 · Cited by 11 · see citing works
An Intelligent Autonomous Robotic System for Precision Farming
Hussain, M., Naqvi, S. H. A., Khan, S. H., & Farhan, M. (2020) · Intelligent Autonomous Systems · doi:10.1109/icoias49312.2020.9081844 · Cited by 10 · see citing works
Usability analysis of scan matching techniques for localization of field machinery in avocado groves
Cheein, F. A., Torres‐Torriti, M., & Rosell-Polo, J. R. (2019) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2019.05.024 · Cited by 10 · see citing works
Lyu, H., Yun, S., & Choi, B. (2020) · Agronomy · doi:10.3390/agronomy10121926 · Cited by 10 · see citing works
Application of Robots and Robotic Systems in Agriculture
Zimmer, D., Jurišić, M., Plašćak, I., Barač, Ž., & Radočaj, D. (2021) · Tehnički glasnik · doi:10.31803/tg-20210128112420 · Cited by 9 · see citing works
Real-Time Computer Vision for Tree Stem Detection and Tracking
Wells, L. A., & Chung, W. (2023) · Forests · doi:10.3390/f14020267 · Cited by 9 · see citing works
de Sousa, R. V., Tabile, R. A., Inamasu, R. Y., & Porto, A. J. V. (2013) · IFAC Proceedings Volumes · doi:10.3182/20130828-2-sf-3019.00020 · Cited by 9 · see citing works
FastSLAM 2.0 running on a low-cost embedded architecture
Abouzahir, M., Elouardi, A., Bouaziz, S., Latif, R., & Tajer, A. (2014) · International Conference on Control, Automation, Robotics and Vision (ICARCV) · doi:10.1109/icarcv.2014.7064524 · Cited by 8 · see citing works
Laser-Based Trespassing Prediction in Restrictive Environments: A Linear Approach
Cheein, F. A., & Scaglia, G. J. E. (2012) · Sensors · doi:10.3390/s120911870 · Cited by 8 · see citing works
A Method to Detect the Occurrence of Rice Plant Lodging Using Wavelet Transform
Masuda, R., Fujimoto, S., Iida, M., & Suguri, M. (2013) · IFAC Proceedings Volumes · doi:10.3182/20130828-2-sf-3019.00048 · Cited by 7 · see citing works
A novel robust approach for SLAM of mobile robot
Ma, J., Zhang, Q., & Ma, L. (2014) · Journal of Central South University · doi:10.1007/s11771-014-2172-4 · Cited by 7 · see citing works
Aerial Drone Mapping and Trajectories Generator for Agricultural Ground Robots
Sulistijono, I. A., Ramadhani, M. R., & Risnumawan, A. (2020) · International Symposium on Community-centric Systems (CcS) · doi:10.1109/ccs49175.2020.9231397 · Cited by 7 · see citing works
Stereovision-Based Ego-Motion Estimation for Combine Harvesters
Chen, H., Chen, J., Guan, Z.-H., Li, Y.-M., Cheng, K., & Cui, Z.-H. (2022) · Italian National Conference on Sensors · doi:10.3390/s22176394 · Cited by 8 · see citing works
Continuous Probabilistic SLAM Solved via Iterated Conditional Modes
Giménez, J., Amicarelli, A., Toibero, J. M., di Sciascio, F. A., & Carelli, R. (2019) · Machine Intelligence Research · doi:10.1007/s11633-019-1186-7 · Cited by 7 · see citing works
Self-citationA Camera/Ultrasonic Sensors Based Trunk Localization System of Semi-Structured Orchards
Yu, Z., Wang, S., & Zhang, B. (2021) · IEEE/ASME International Conference on Advanced Intelligent Mechatronics · doi:10.1109/aim46487.2021.9517543 · Cited by 6 · see citing works
Special Sensors for Autonomous Navigation Systems in Crops Investigation System
Kaswan, K. S., Dhatterwal, J. S., Baliyan, A., & Jain, V. K. (2022) · Studies in systems, decision and control · doi:10.1007/978-3-030-94102-4_4 · Cited by 6 · see citing works
Hiraoka, R., Aoyagi, Y., & Kobayashi, K. (2023) · Journal of Agricultural Engineering · doi:10.4081/jae.2023.1355 · Cited by 6 · see citing works
Information System for Detecting Strawberry Fruit Locations and Ripeness Conditions in a Farm
Liu, T., Chopra, N., & Samtani, J. B. (2022) · International Electronic Conference on Horticulturae (IECHo) · doi:10.3390/iecho2022-12488 · Cited by 5 · see citing works
FloPE: Flower Pose Estimation for Precision Pollination
Shrestha, R., Rijal, M., Smith, T., & Gu, Y. (2025) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros60139.2025.11247634 · Cited by 4 · see citing works
SLAM-based maneuverability strategy for unmanned car-like vehicles
Cheein, F. A. (2013) · Robotica · doi:10.1017/s0263574713000131 · Cited by 4 · see citing works
Klančar, G., Zdešar, A., Blažič, S., & Škrjanc, I. (2017) · Elsevier eBooks · doi:10.1016/b978-0-12-804204-5.00007-x · Cited by 4 · see citing works
Simões, J. P., Gaspar, P. D., Assunção, E., Mesquita, R. P., & Simões, M. P. (2022) · Acta Horticulturae · doi:10.17660/actahortic.2022.1352.80 · Cited by 4 · see citing works
PlantPal: Leveraging Precision Agriculture Robots to Facilitate Remote Engagement in Urban Gardening
Zeqiri, A., Britten, J., Schramm, C., Jansen, P., Rietzler, M., & Rukzio, E. (2025) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713180 · Cited by 4 · see citing works
A comprehensive review of current robot-based pollinators for crop pollination
Singh, R., Seneviratne, L. D., & Hussain, I. (2025) · Artificial Intelligence Review · doi:10.1007/s10462-025-11409-1 · Cited by 3 · see citing works
CK-SLAM, Crop-Row and Kinematics-Constrained SLAM for Quadruped Robots Under Corn Canopies
Wan, M., Luo, X., Wu, J., Li, L., Tang, R., Peng, Z., 蒋娟萍, Zhou, S., & Liu, Z. L. (2025) · Agronomy · doi:10.3390/agronomy16010095 · Cited by 3 · see citing works
A Trunk Map Construction Method for Long-Term Localization and Navigation for Orchard Robots
Liu, E., Lou, J., Tang, W., Wu, J., & Liang, X. (2023) · Lecture notes in computer science · doi:10.1007/978-981-99-6483-3_38 · Cited by 3 · see citing works
Automatic Method for Extracting Tree Branching Structures from a Single RGB Image
Yang, Y., Lai, H., Chen, B., Huo, Y., Xia, K., & Huang, J. (2024) · Forests · doi:10.3390/f15091659 · Cited by 2 · see citing works
Global self-localization and navigation using 3D-LiDAR for headland turning in vineyards
Usu, K., Yamasaki, Y., Ishii, K., & Noguchi, N. (2025) · Smart Agricultural Technology · doi:10.1016/j.atech.2025.101296 · Cited by 2 · see citing works
Nazate-Burgos, P., Torres‐Torriti, M., Huang, S., & Cheein, F. A. (2026) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2026.111687 · Cited by 2 · see citing works
Covariance-Based Measurement Selection Criterion for Gaussian-Based Algorithms
Cheein, F. A. (2013) · Entropy · doi:10.3390/e15010287 · Cited by 2 · see citing works
- Authors
- Auat Cheein, F., Steiner, G., Perez Paina, G., & Carelli, R.
- Affiliations
- National Technological University (AR)
- National University of San Juan (AR)
- ORCID
- Carelli, R.
- Venue
- Computers and Electronics in Agriculture
- Publisher
- Elsevier BV
- Published
- 2011-09
- Volume
- 78
- Issue
- 2
- Pages
- 195-207
- ISSN
- 0168-1699 (ISSN-L)
- OpenAlex
- W2048269893
- Cited by
- 118
- References
- 37
- Citations by year
- 2026: 1 · 2025: 7 · 2024: 4 · 2023: 11 · 2022: 13 · 2021: 10 · 2020: 14 · 2019: 8 · 2018: 14 · 2017: 6 · 2016: 8 · 2015: 8 · 2014: 4 · 2013: 9 · 2012: 1
- FWCI
- 8.05
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Computers and Electronics in Agriculture (publishedVersion)
- Topics
- Physical Sciences › Engineering › Aerospace Engineering › Robotics and Sensor-Based Localization
- Life Sciences › Agricultural and Biological Sciences › Plant Science › Smart Agriculture and AI
- Physical Sciences › Engineering › Aerospace Engineering › Inertial Sensor and Navigation
- Keywords
- extended information filter, SLAM, olive groves, stem detection, support vector machine, mobile robot navigation, laser range sensor, monocular vision, agricultural robotics
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