nach oben

Erschienen in:

2022 | OriginalPaper | Buchkapitel

3D Scenery Construction of Agricultural Environments for Robotics Awareness

verfasst von : Aristotelis Christos Tagarakis, Damianos Kalaitzidis, Evangelia Filippou, Lefteris Benos, Dionysis Bochtis

Erschienen in: Information and Communication Technologies for Agriculture—Theme III: Decision

Verlag: Springer International Publishing

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

Depth cameras started to gain popularity in agricultural applications during the last years. This type of cameras has been implemented mainly for three-dimensional (3D) reconstruction of objects in indoor or outdoor sceneries. The use of such cameras in the construction of 3D models for simulation purposes of complex structures that are usually met in nature, such as trees and other plants, is a great challenge. Remarkably, agricultural environments are extremely complex. Thus, the proper setup and implementation of such technologies is particularly important in order to attain useable data. So far, the depth information collected using these cameras varies among different objects’ structure and sensing conditions due to the uncertainty of the outdoor environment. The use of a specific methodology using color and depth images gives the opportunity to extract geometrical characteristics information about point clouds of the targeted objects. This chapter explores the different technologies used by depth cameras and presents several applications concerning indoor and outdoor environments by presenting indicative scenarios for agricultural applications. Towards that direction, a 3D reconstruction of trees was established producing point clouds from Red Green Blue Depth (RGB-D) images acquired in real field conditions. The point cloud samples of trees were collected using an unmanned ground vehicle (UGV) and imported in Gazebo in order to visualize a simulation of the environment. This simulation technique can be used for testing and evaluating the navigation of robotic systems. By further analyzing the resulted 3D point clouds, various geometrical measurements of the simulated samples, such as the volume or the height of tree canopies, can be calculated. Possible weaknesses of this procedure are mainly attributed to the camera’s limitations and the sampling parameters. However, results show that it is possible to establish a suitable simulation environment to implement it in several agricultural applications by utilizing automated unmanned robotic platforms.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Vorheriges Kapitel On the Routing of Unmanned Aerial Vehicles (UAVs) in Precision Farming Sampling Missions

Nächstes Kapitel A Weed Control Unmanned Ground Vehicle Prototype for Precision Farming Activities: The Case of Red Rice

Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al. (2011). KinectFusion: Real-time 3D reconstruction and interaction using a moving depth camera. In Proceedings of the UIST’11 - Proceedings of the 24th annual ACM symposium on user interface software and technology. ACM Press, New York, NY, pp. 559–568.

Pan, H., Guan, T., Luo, Y., Duan, L., Tian, Y., Yi, L., Zhao, Y., & Yu, J. (2016). Dense 3D reconstruction combining depth and RGB information. Neurocomputing, 175, 644–651. https://doi.org/10.1016/j.neucom.2015.10.104CrossRef

Biskup, B., Scharr, H., Schurr, U., & Rascher, U. (2007). A stereo imaging system for measuring structural parameters of plant canopies. Plant, Cell and Environment, 30, 1299–1308. https://doi.org/10.1111/j.1365-3040.2007.01702.xCrossRef

Schöps, T., Sattler, T., Häne, C., & Pollefeys, M. (2017). Large-scale outdoor 3D reconstruction on a mobile device. Computer Vision and Image Understanding, 157, 151–166. https://doi.org/10.1016/j.cviu.2016.09.007CrossRef

Li, J., Huang, W., Shao, L., & Allinson, N. (2014). Building recognition in urban environments: A survey of state-of-the-art and future challenges. Information Sciences (NY), 277, 406–420. https://doi.org/10.1016/j.ins.2014.02.112CrossRef

Guo, Y., Sohel, F., Bennamoun, M., Wan, J., & Lu, M. (2015). A novel local surface feature for 3D object recognition under clutter and occlusion. Information Sciences (NY), 293, 196–213. https://doi.org/10.1016/j.ins.2014.09.015CrossRef

Kim, D. Y., & Jeon, M. (2014). Data fusion of radar and image measurements for multi-object tracking via Kalman filtering. Information Sciences (NY), 278, 641–652. https://doi.org/10.1016/j.ins.2014.03.080MathSciNetCrossRef

Yang, D., Liu, Z., Sun, F., Zhang, J., Liu, H., & Wang, S. (2014). Recursive depth parametrization of monocular visual navigation: Observability analysis and performance evaluation. Information Sciences (NY), 287, 38–49. https://doi.org/10.1016/j.ins.2014.07.025MathSciNetCrossRefMATH

Viejo, D., Garcia-Rodriguez, J., & Cazorla, M. (2014). Combining visual features and Growing Neural Gas networks for robotic 3D SLAM. Information Sciences (NY), 276, 174–185. https://doi.org/10.1016/j.ins.2014.02.053CrossRef

10.

Gálvez, A., & Iglesias, A. (2012). Particle swarm optimization for non-uniform rational B-spline surface reconstruction from clouds of 3D data points. Information Sciences (NY), 192, 174–192. https://doi.org/10.1016/j.ins.2010.11.007CrossRef

11.

Nevatia, R. (1976). Depth measurement by motion stereo. Comput. Graph. Image Process., 5, 203–214. https://doi.org/10.1016/0146-664X(76)90028-9CrossRef

12.

Mrovlje, J., & Vrani, D. (2008). Distance measuring based on stereoscopic pictures. In 9th Int. PhD Work. Syst. Control Young Gener. Viewp.

13.

Liu, Z., & Chen, T. (2009). Distance measurement system based on binocular stereo vision. In Proceedings of the IJCAI international joint conference on artificial intelligence; pp. 456–459.

14.

Condotta, I. C. F. S., Brown-Brandl, T. M., Pitla, S. K., Stinn, J. P., & Silva-Miranda, K. O. (2020). Evaluation of low-cost depth cameras for agricultural applications. Computers and Electronics in Agriculture, 173, 105394. https://doi.org/10.1016/j.compag.2020.105394CrossRef

15.

Bertheloot, J., Cournède, P.-H., & Andrieu, B. PART OF A SPECIAL ISSUE ON FUNCTIONAL-STRUCTURAL PLANT MODELLING—NEMA, a functional-structural model of nitrogen economy within wheat culms after flowering. I. Model description. Annals of Botany, 108, 1085–1096. https://doi.org/10.1093/aob/mcr119

16.

Vos, J., Evers, J. B., Buck-Sorlin, G. H., Andrieu, B., Chelle, M., & De Visser, P. H. B. (2010). Functional-structural plant modelling: A new versatile tool in crop science. Journal of Experimental Botany, 61, 2101–2115.CrossRef

17.

Zhu, C., Zhang, X., Hu, B., & Jaeger, M. (2008). Reconstruction of tree crown shape from scanned data. In Proceedings of the lecture notes in computer science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Springer, Berlin, Heidelberg, Vol. 5093 LNCS, pp. 745–756.

18.

Omasa, K., Hosoi, F., & Konishi, A. (2007). 3D lidar imaging for detecting and understanding plant responses and canopy structure. Journal of Experimental Botany, 58, 881–898.CrossRef

19.

Binney, J., & Sukhatme, G. S. (2009). 3D tree reconstruction from laser range data. In Proceedings of the proceedings - IEEE international conference on robotics and automation, pp. 1321–1326.

20.

Chéné, Y., Rousseau, D., Lucidarme, P., Bertheloot, J., Caffier, V., Morel, P., Belin, É., & Chapeau-Blondeau, F. (2012). On the use of depth camera for 3D phenotyping of entire plants. Computers and Electronics in Agriculture, 82, 122–127. https://doi.org/10.1016/j.compag.2011.12.007CrossRef

21.

Andújar, D., Dorado, J., Fernández-Quintanilla, C., & Ribeiro, A. (2016). An approach to the use of depth cameras for weed volume estimation. Sensors, 16, 972. https://doi.org/10.3390/s16070972CrossRef

22.

Orriordan, A., Newe, T., Dooly, G., & Toal, D. (2019). Stereo vision sensing: Review of existing systems. In Proceedings of the Proceedings of the international conference on sensing technology, ICST; IEEE computer society, Vol. 2018-Decem, pp. 178–184.

23.

Tran, V. L., & Lin, H. Y. (2018). A structured light RGB-D camera system for accurate depth measurement. International Journal of Optics, 2018. https://doi.org/10.1155/2018/8659847

24.

Lachat, E., Macher, H., Mittet, M.-A., Landes, T., & Grussenmeyer, P. (2015). FIRST EXPERIENCES WITH KINECT V2 SENSOR FOR CLOSE RANGE 3D MODELLING. In ISPRS - Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci., XL-5/W4, 93–100. https://doi.org/10.5194/isprsarchives-XL-5-W4-93-2015.

25.

Kirsten, E., Inocencio, L. C., Veronez, M. R., Da Silveira, L. G., Bordin, F., & Marson, F. P. (2018). 3D data acquisition using stereo camera. In Proceedings of the international geoscience and remote sensing symposium (IGARSS), Institute of Electrical and Electronics Engineers Inc., Vol. 2018-July, pp. 9214–9217.

26.

Marin, G., Agresti, G., Minto, L., & Zanuttigh, P. (2019). A multi-camera dataset for depth estimation in an indoor scenario. Open Data BR, 27, 104619. https://doi.org/10.1016/j.dib.2019.104619CrossRef

27.

Endres, F., Hess, J., Sturm, J., Cremers, D., & Burgard, W. (2014). 3-D Mapping with an RGB-D camera. IEEE Transactions on Robotics, 30, 177–187. https://doi.org/10.1109/TRO.2013.2279412CrossRef

28.

Tran, T. M., Ta, K. D., Hoang, M., Nguyen, T. V., Nguyen, N. D., & Pham, G. N. (2020). A study on determination of simple objects volume using ZED stereo camera based on 3D-points and segmentation images. International Journal of Emerging Engineering Research and Technology, 8, 1990–1995. https://doi.org/10.30534/ijeter/2020/85852020CrossRef

29.

Sarker, M. M., Ali, T. A., Abdelfatah, A., Yehia, S., & Elaksher, A. (2017). A cost-effective method for crack detection and measurement on concrete surface. ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Science, XLII-2/W8, 237–241. https://doi.org/10.5194/isprs-archives-XLII-2-W8-237-2017CrossRef

30.

Burdziakowski, P. (2018). Low cost real time UAV stereo photogrammetry modelling technique-accuracy considerations. In Proceedings of the E3S web of conferences, EDP sciences, Vol. 63, p. 00020.

31.

Gupta, T., & Li, H. (2017). Indoor mapping for Smart Cities-An affordable approach: Using kinect sensor and ZED stereo camera. In Proceedings of the 2017 international conference on indoor positioning and indoor navigation, IPIN 2017, Institute of Electrical and Electronics Engineers Inc., Vol. 2017-Janua, pp. 1–8.

32.

Halmetschlager-Funek, G., Suchi, M., Kampel, M., & Vincze, M. (2019). An empirical evaluation of ten depth cameras: Bias, precision, lateral noise, different lighting conditions and materials, and multiple sensor setups in indoor environments. IEEE Robotics and Automation Magazine, 26, 67–77. https://doi.org/10.1109/MRA.2018.2852795CrossRef

33.

Wang, W., & Li, C. (2014). Size estimation of sweet onions using consumer-grade RGB-depth sensor. Journal of Food Engineering, 142, 153–162. https://doi.org/10.1016/j.jfoodeng.2014.06.019CrossRef

34.

Wang, Q., & Zhang, Q. (2013). Three-dimensional reconstruction of a dormant tree using RGB-D Cameras. In Proceedings of the 2013 Kansas City, Missouri, July 21–July 24, 2013, American Society of Agricultural and Biological Engineers, St. Joseph, MI, Vol. 2, pp. 1341–1350.

35.

Andújar, D., Ribeiro, A., Fernández-Quintanilla, C., & Dorado, J. (2016). Using depth cameras to extract structural parameters to assess the growth state and yield of cauliflower crops. Computers and Electronics in Agriculture, 122, 67–73. https://doi.org/10.1016/j.compag.2016.01.018CrossRef

36.

van der Heijden, G., Song, Y., Horgan, G., Polder, G., Dieleman, A., Bink, M., Palloix, A., van Eeuwijk, F., & Glasbey, C. (2012). SPICY: Towards automated phenotyping of large pepper plants in the greenhouse. Functional Plant Biology, 39, 870. https://doi.org/10.1071/FP12019CrossRef

37.

Kazmi, W., Foix, S., & Alenyà, G. (2012). Plant leaf imaging using time of flight camera under sunlight, shadow and room conditions. In Proceedings of the 2012 IEEE International Symposium on Robotic and Sensors Environments, ROSE 2012 – Proceedings, IEEE Computer Society, pp. 192–197.

38.

Swain, K. C., Zaman, Q. U., Schumann, A. W., Percival, D. C., & Bochtis, D. D. (2010). Computer vision system for wild blueberry fruit yield mapping. Biosystems Engineering, 106. https://doi.org/10.1016/j.biosystemseng.2010.05.001

39.

Mai, C., Zheng, L., Sun, H., & Yang, W. (2015). Research on 3D reconstruction of fruit tree and fruit recognition and location method based on RGB-D camera. Nongye Jixie Xuebao/Transactions Chinese Soc. Agricultural Machinery, 46, 35–40. https://doi.org/10.6041/j.issn.1000-1298.2015.S0.006CrossRef

40.

Nguyen, T. T., Vandevoorde, K., Wouters, N., Kayacan, E., De Baerdemaeker, J. G., & Saeys, W. (2016). Detection of red and bicoloured apples on tree with an RGB-D camera. Biosystems Engineering, 146, 33–44. https://doi.org/10.1016/j.biosystemseng.2016.01.007CrossRef

41.

Tian, Y., Duan, H., Luo, R., Zhang, Y., Jia, W., Lian, J., Zheng, Y., Ruan, C., & Li, C. (2019). Fast recognition and location of target fruit based on depth information. IEEE Access, 7, 170553–170563. https://doi.org/10.1109/ACCESS.2019.2955566CrossRef

42.

Gené-Mola, J., Gregorio, E., Auat Cheein, F., Guevara, J., Llorens, J., Sanz-Cortiella, R., Escolà, A., & Rosell-Polo, J. R. (2020). Fruit detection, yield prediction and canopy geometric characterization using LiDAR with forced air flow. Computers and Electronics in Agriculture, 168, 105121. https://doi.org/10.1016/j.compag.2019.105121CrossRef

43.

Häni, N., Roy, P., & Isler, V. (2020). A comparative study of fruit detection and counting methods for yield mapping in apple orchards. Journal of Field Robotics, 37, 263–282. https://doi.org/10.1002/rob.21902CrossRef

44.

Bochtis, D. D., Sørensen, C. G., Jørgensen, R. N., Nørremark, M., Hameed, I. A., & Swain, K. C. (2011). Robotic weed monitoring. Acta Agriculturae Scandinavica Section B Soil and Plant Science, 61. https://doi.org/10.1080/09064711003796428

45.

Anagnostis, A., Tagarakis, A. C., Asiminari, G., Papageorgiou, E., Kateris, D., Moshou, D., & Bochtis, D. (2021). A deep learning approach for anthracnose infected trees classification in walnut orchards. Computers and Electronics in Agriculture, 182, 105998. https://doi.org/10.1016/j.compag.2021.105998CrossRef

46.

Anagnostis, A., Asiminari, G., Papageorgiou, E., & Bochtis, D. (2020). A convolutional neural networks based method for anthracnose infected walnut tree leaves identification. Applied Sciences, 10. https://doi.org/10.3390/app10020469

47.

Fu, L., Gao, F., Wu, J., Li, R., Karkee, M., & Zhang, Q. (2020). Application of consumer RGB-D cameras for fruit detection and localization in field: A critical review. Computers and Electronics in Agriculture, 177, 105687.CrossRef

48.

Kise, M., & Zhang, Q. (2008). Development of a stereovision sensing system for 3D crop row structure mapping and tractor guidance. Biosystems Engineering, 101, 191–198. https://doi.org/10.1016/j.biosystemseng.2008.08.001CrossRef

49.

Hornung, A., Wurm, K. M., Bennewitz, M., Stachniss, C., & Burgard, W. (2013). OctoMap: An efficient probabilistic 3D mapping framework based on octrees. Autonomous Robots, 34, 189–206. https://doi.org/10.1007/s10514-012-9321-0CrossRef

50.

De Silva, K. T. D. S., Cooray, B. P. A., Chinthaka, J. I., Kumara, P. P., & Sooriyaarachchi, S. J. (2019). Comparative analysis of octomap and RTABMap for multi-robot disaster site mapping (pp. 433–438). Institute of Electrical and Electronics Engineers (IEEE).

51.

Wang, Z. (2020). Digital twin technology. In Industry 4.0 - Impact on Intelligent Logistics and Manufacturing, IntechOpen.

52.

Glaessgen, E. H., & Stargel, D. S. (2012). The digital twin paradigm for future NASA and U.S. Air force vehicles. In Proceedings of the Collection of Technical Papers - AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference.

53.

Vachalek, J., Bartalsky, L., Rovny, O., Sismisova, D., Morhac, M., & Loksik, M. (2017). The digital twin of an industrial production line within the industry 4.0 concept. In Proceedings of the proceedings of the 2017 21st international conference on process control, PC 2017, Institute of Electrical and Electronics Engineers Inc., pp. 258–262.

54.

Knapp, G. L., Mukherjee, T., Zuback, J. S., Wei, H. L., Palmer, T. A., De, A., & DebRoy, T. (2017). Building blocks for a digital twin of additive manufacturing. Acta Materialia, 135, 390–399. https://doi.org/10.1016/j.actamat.2017.06.039CrossRef

55.

Söderberg, R., Wärmefjord, K., Carlson, J. S., & Lindkvist, L. (2017). Toward a Digital Twin for real-time geometry assurance in individualized production. CIRP Annual, 66, 137–140. https://doi.org/10.1016/j.cirp.2017.04.038CrossRef

56.

Rosen, R., Von Wichert, G., Lo, G., & Bettenhausen, K. D. (2015). About the importance of autonomy and digital twins for the future of manufacturing. Proceedings of the IFAC-Papers, 28, 567–572.CrossRef

57.

Gabor, T., Belzner, L., Kiermeier, M., Beck, M. T., & Neitz, A. (2016). A simulation-based architecture for smart cyber-physical systems. In Proceedings of the Proceedings – 2016 IEEE International Conference on Autonomic Computing, ICAC 2016, Institute of Electrical and Electronics Engineers Inc., pp. 374–379.

58.

Kang, H. S., Lee, J. Y., Choi, S., Kim, H., Park, J. H., Son, J. Y., Kim, B. H., & Noh, S. (2016). Do Smart manufacturing: Past research, present findings, and future directions. International Journal of Precision Engineering and Manufacturing-Green Technology, 3, 111–128. https://doi.org/10.1007/s40684-016-0015-5CrossRef

59.

Wang, L., Törngren, M., & Onori, M. (2015). Current status and advancement of cyber-physical systems in manufacturing. Journal of Manufacturing Systems, 37, 517–527. https://doi.org/10.1016/j.jmsy.2015.04.008CrossRef

60.

Tao, F., Zhang, L., & Laili, Y. (2015). CLPS-GA for energy-aware cloud service scheduling (pp. 191–224).

61.

Zhang, M., Sui, F., Liu, A., Tao, F., & Nee, A. Y. C. (2020). Digital twin driven smart product design framework. In Digital twin driven smart design (pp. 3–32). Elsevier.CrossRef

62.

Kousi, N., Gkournelos, C., Aivaliotis, S., Giannoulis, C., Michalos, G., & Makris, S. (2019). Digital twin for adaptation of robots’ behavior in flexible robotic assembly lines. In Proceedings of the procedia manufacturing (Vol. 28, pp. 121–126). Elsevier B.V.

63.

Moshrefzadeh, M., Machl, T., Gackstetter, D., Donaubauer, A., & Kolbe, T. H. (2020). Towards a distributed digital twin of the agricultural landscape. Journal of Digital Landscape Architecture, 5, 173–118.

64.

Alves, R. G., Souza, G., Maia, R. F., Tran, A. L. H., Kamienski, C., Soininen, J. P., Aquino, P. T., & Lima, F. (2019). A digital twin for smart farming. In Proceedings of the 2019 IEEE global humanitarian technology conference, GHTC 2019, Institute of Electrical and Electronics Engineers Inc.

65.

Verdouw, C., & Kruize, J. W. (2017). Digital twins in farm management: Illustrations from the FIWARE accelerators SmartAgriFood and Fractals. In Proceedings of the 7th Asian-Australasian conference on precision agriculture, pp. 1–5.

66.

Qian, W., Xia, Z., Xiong, J., Gan, Y., Guo, Y., Weng, S., Deng, H., Hu, Y., & Zhang, J. (2014). Manipulation task simulation using ROS and Gazebo. In Proceedings of the 2014 IEEE international conference on robotics and biomimetics, IEEE ROBIO 2014, Institute of Electrical and Electronics Engineers Inc., pp. 2594–2598.

67.

Edwards, G., Christiansen, M., Bochtis, D., & Sørensen, C. (2013). A test platform for planned field operations using LEGO mindstorms NXT. Robotics, 2, 203–216. https://doi.org/10.3390/robotics2040203CrossRef

68.

Moysiadis, V., Tsolakis, N., Katikaridis, D., Sørensen, C. G., Pearson, S., & Bochtis, D. (2020). Mobile robotics in agricultural operations: A narrative review on planning aspects. Applied Sciences, 10, 3453. https://doi.org/10.3390/app10103453CrossRef

69.

Anagnostis, A., Benos, L., Tsaopoulos, D., Tagarakis, A., Tsolakis, N., & Bochtis, D. (2021). Human activity recognition through reccurent neural networks for human-robot interaction. Applied Sciences, 11(5), 2188. https://doi.org/10.3390/app11052188

70.

Benos, L., Bechar, A., & Bochtis, D. (2020). Safety and ergonomics in human-robot interactive agricultural operations. Biosystems Engineering, 200, 55–72. https://doi.org/10.1016/j.biosystemseng.2020.09.009CrossRef

71.

Benos, L., Tsaopoulos, D., & Bochtis, D. (2020). A review on ergonomics in agriculture. Part I: Manual operations. Applied Sciences, 10, 1–21.

72.

Benos, L., Tsaopoulos, D., & Bochtis, D. (2020). A review on ergonomics in agriculture. part II: Mechanized operations. Applied Sciences, 10, 3484.CrossRef

73.

Marinoudi, V., Sørensen, C. G., Pearson, S., & Bochtis, D. (2019). Robotics and labour in agriculture. A context consideration. Biosystems Engineering, 184, 111–121. https://doi.org/10.1016/j.biosystemseng.2019.06.013CrossRef

74.

Marinoudi, V., Lampridi, M., Sørensen, C. G., Pearson, S., & Bochtis, D. (2021). The agricultural occupations landscape in view of work automation. In Bio-economy and agri-production (pp. 289–348). Elsevier.CrossRef

75.

Shamshiri, R. R., Hameed, I. A., Pitonakova, L., Weltzien, C., Balasundram, S. K., Yule, I. J., Grift, T. E., & Chowdhary, G. (2018). Simulation software and virtual environments for acceleration of agricultural robotics: Features highlights and performance comparison. International Journal of Agricultural and Biological Engineering, 11, 15–31. https://doi.org/10.25165/ijabe.v11i4.4032CrossRef

76.

Tsolakis, N., Bechtsis, D., & Bochtis, D. (2019). Agros: A robot operating system based emulation tool for agricultural robotics. Agronomy. https://doi.org/10.3390/agronomy9070403

77.

Lavrenov, R., Zakiev, A., & Magid, E. (2017). Automatic mapping and filtering tool: From a sensor-based occupancy grid to a 3D Gazebo octomap. In Proceedings of the 2017 international conference on mechanical, system and control engineering, ICMSC 2017, Institute of Electrical and Electronics Engineers Inc., pp. 190–195.

78.

Guzman, R., Navarro, R., Beneto, M., & Carbonell, D. (2016). Robotnik—Professional service robotics applications with ROS. Studies in Computational Intelligence, 625, 253–288. https://doi.org/10.1007/978-3-319-26054-9_10CrossRef

Titel: 3D Scenery Construction of Agricultural Environments for Robotics Awareness
verfasst von: Aristotelis Christos Tagarakis
Damianos Kalaitzidis
Evangelia Filippou
Lefteris Benos
Dionysis Bochtis
Verlag: Springer International Publishing
Buch: Information and Communication Technologies for Agriculture—Theme III: Decision
Print ISBN: 978-3-030-84151-5

Electronic ISBN: 978-3-030-84152-2

Copyright-Jahr: 2022
DOI: https://doi.org/10.1007/978-3-030-84152-2_6

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Premium Partner