Abstract
This paper introduces a model based detection approach for typical structures in the agricultural environment. In contrast to other existing approaches in this area it exclusively relies on distance data information generated by a laser scanner which makes the detection robust against varying illumination. Additionally, the method was designed to be easily adaptable to different agricultural structures as well as to minimize computation power. To test the performance and the capabilities of the presented approach a prototypical baling assistance system was implemented where a tractor had to follow a straw windrow while the implement created round bales.
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Barawid OC, Mizushima A, Ishii K, Noguchi N (2007) Development of an autonomous navigation system using a two-dimensional laser scanner in an orchard application. Biosyst Eng 96(2):139–149
Blas MR (2010) Fault-tolerant vision for vehicle guidance in agriculture. Ph.D. thesis, Department of Electrical Engineering, Automation and Control, Technical University of Denmark
Chateau T, Debain C, Collange F, Trassoudaine L, Alizon J (2000) Automatic guidance of agricultural vehicles using a laser sensor. Comput Electron Agric 28(3):243–257
Edan Y, Han S, Kondo N (2009) In: Springer handbook of automation, automation in agriculture. Springer, Berlin, p 1096. Chap. 63
Fischler MA, Bolles RC (1981) Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun ACM 24(6):381–395
Gan-Mor S, Clark RL, Upchurch BL (2007) Implement lateral position accuracy under rtk-gps tractor guidance. Comput Electron Agric 59(1–2):31–38
Isard M, Blake A (1998) Condensation—conditional density propagation for visual tracking. Int J Comput Vis 28(1):5–28
Kise M, Zhang Q, Más FR (2005) A stereovision-based crop row detection method for tractor-automated guidance. Biosyst Eng 90(4):357–367
Kise M, Zhang Q, Noguchi N (2005) An obstacle identification algorithm for a laser range finder-based obstacle detector. Trans ASABE 48(3):1269–1278
Lenaerts B, Missotten B, Baerdemaeker JD, Saeys W (2012) Lidar sensing to monitor straw output quality of a combine harvester. Comput Electron Agric 85:40–44
Peters O (2010) Kooperative Führung von Landmaschinen mittels LIDAR. Ph.D. thesis, University Halle-Wittenberg
Pilarski T, Happold M, Pangels H, Ollis M, Fitzpatrick K, Stentz AT (1999) The demeter system for automated harvesting. In: Proceedings of the 8th international topical meeting on robotics and remote systems
Reichardt M, Föhst T, Berns K (2013) On software quality-motivated design of a real-time framework for complex robot control systems. In: Proceedings of the 7th international workshop on software quality and maintainability (SQM), in conjunction with the 17th European conference on software maintenance and reengineering (CSMR), Genoa, Italy
Whipker L, Akridge JT (2009) precision agricultural services dealership survey results. Tech. Rep. 09-16, Purdue University, College of Agriculture, Department of Agricultural Economics
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Fleischmann, P., Föhst, T. & Berns, K. Detection of Field Structures for Agricultural Vehicle Guidance. Künstl Intell 27, 351–357 (2013). https://doi.org/10.1007/s13218-013-0264-1
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DOI: https://doi.org/10.1007/s13218-013-0264-1