Multi-Modal Detection and Mapping of Static and Dynamic Obstacles in Agriculture for Process Evaluation

Korthals T, Kragh M, Christiansen P, Karstoft H, Jørgensen RN, Rückert U (2018)
Frontiers in Robotics and AI 5: 26.

Zeitschriftenaufsatz | Veröffentlicht | Englisch
 
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Abstract / Bemerkung
Today, agricultural vehicles are available that can automatically perform tasks such as weed detection and spraying, mowing, and sowing while being steered automatically. However, for such systems to be fully autonomous and self-driven, not only their specific agricultural tasks must be automated. An accurate and robust perception system automatically detecting and avoiding all obstacles must also be realized to ensure safety of humans, animals, and other surroundings. In this paper, we present a multi-modal obstacle and environment detection and recognition approach for process evaluation in agricultural fields. The proposed pipeline detects and maps static and dynamic obstacles globally, while providing process-relevant information along the traversed trajectory. Detection algorithms are introduced for a variety of sensor technologies, including range sensors (lidar and radar) and cameras (stereo and thermal). Detection information is mapped globally into semantical occupancy grid maps and fused across all sensors with late fusion, resulting in accurate traversability assessment and semantical mapping of process-relevant categories (e.g., crop, ground, and obstacles). Finally, a decoding step uses a Hidden Markov model to extract relevant process-specific parameters along the trajectory of the vehicle, thus informing a potential control system of unexpected structures in the planned path. The method is evaluated on a public dataset for multi-modal obstacle detection in agricultural fields. Results show that a combination of multiple sensor modalities increases detection performance and that different fusion strategies must be applied between algorithms detecting similar and dissimilar classes.
Erscheinungsjahr
2018
Zeitschriftentitel
Frontiers in Robotics and AI
Band
5
Art.-Nr.
26
ISSN
2296-9144
Finanzierungs-Informationen
Article Processing Charge funded by the Deutsche Forschungsgemeinschaft and the Open Access Publication Fund of Bielefeld University.
Page URI
https://pub.uni-bielefeld.de/record/2918982

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Korthals T, Kragh M, Christiansen P, Karstoft H, Jørgensen RN, Rückert U. Multi-Modal Detection and Mapping of Static and Dynamic Obstacles in Agriculture for Process Evaluation. Frontiers in Robotics and AI. 2018;5: 26.
Korthals, T., Kragh, M., Christiansen, P., Karstoft, H., Jørgensen, R. N., & Rückert, U. (2018). Multi-Modal Detection and Mapping of Static and Dynamic Obstacles in Agriculture for Process Evaluation. Frontiers in Robotics and AI, 5, 26. doi:10.3389/frobt.2018.00028
Korthals, T., Kragh, M., Christiansen, P., Karstoft, H., Jørgensen, R. N., and Rückert, U. (2018). Multi-Modal Detection and Mapping of Static and Dynamic Obstacles in Agriculture for Process Evaluation. Frontiers in Robotics and AI 5:26.
Korthals, T., et al., 2018. Multi-Modal Detection and Mapping of Static and Dynamic Obstacles in Agriculture for Process Evaluation. Frontiers in Robotics and AI, 5: 26.
T. Korthals, et al., “Multi-Modal Detection and Mapping of Static and Dynamic Obstacles in Agriculture for Process Evaluation”, Frontiers in Robotics and AI, vol. 5, 2018, : 26.
Korthals, T., Kragh, M., Christiansen, P., Karstoft, H., Jørgensen, R.N., Rückert, U.: Multi-Modal Detection and Mapping of Static and Dynamic Obstacles in Agriculture for Process Evaluation. Frontiers in Robotics and AI. 5, : 26 (2018).
Korthals, Timo, Kragh, Mikkel, Christiansen, Peter, Karstoft, Henrik, Jørgensen, Rasmus N., and Rückert, Ulrich. “Multi-Modal Detection and Mapping of Static and Dynamic Obstacles in Agriculture for Process Evaluation”. Frontiers in Robotics and AI 5 (2018): 26.
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2019-09-06T09:18:58Z
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