Abstract
For single-plant specific weed regulation, robotic systems and agricultural machinery in general have to collect a large amount of temporal and spatial high-resolution sensor data. SEEREP, the Spatio-Temporal-Semantic Environment Representation, can be used to structure and manage such data more efficiently. SEEREP deals with the spatial, temporal and semantic modalities of data simultaneously and provides an efficient query interface for all three modalities that can be combined for high-level analyses. It supports popular robotic sensor data such as images and point clouds, as well as sensor and robot coordinate frames changing over time. This query interface enables high-level reasoning systems as well as other data analysis methods to handle partially unstructured environments that change over time, as for example agricultural environments. But the current methodology of SEEREP cannot store the result of the analysis methods regarding specific objects instances in the world. Especially the results of the anchoring problem which searches for a connection between symbolic and sub-symbolic data cannot be represented nor queried. Thus, we propose a further development of the SEEREP methodology in this paper: For a given object, we link the existing semantic labels in different datasets to a unique common instance, thereby enabling queries for datasets showing this object instance and with this enabling the efficient provision of datasets for object-centric analysis algorithms. Additionally, the results of those algorithms can be stored linked to the instance either by adding facts in a triple-store like manner or by adding further data linked to the instance, like a point, representing the position of the instance. We show the benefits of our anchoring approach in an agricultural setting with the use-case of single-plant specific weed regulation.
The DFKI Niedersachsen (DFKI NI) is sponsored by the Ministry of Science and Culture of Lower Saxony and the VolkswagenStiftung. This work is supported by the Federal Ministry for the Environment, Nature Conservation, Nuclear Safety and Consumer Protection (BMUV) within the CognitiveWeeding project (grant number: 67KI21001B).
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Deeken, H., Wiemann, T., Hertzberg, J.: Grounding semantic maps in spatial databases. Robot. Auton. Syst. 105, 146–165 (2018). https://doi.org/10.1016/j.robot.2018.03.011
Dong, J., Burnham, J.G., Boots, B., Rains, G., Dellaert, F.: 4D crop monitoring: Spatio-temporal reconstruction for agriculture. In: 2017 IEEE ICRA, pp. 3878–3885. Singapore (2017). https://doi.org/10.1109/ICRA.2017.7989447
Elfring, J., van den Dries, S., van de Molengraft, M.J.G., Steinbuch, M.: Semantic world modeling using probabilistic multiple hypothesis anchoring. Robot. Auton. Syst. 61(2), 95–105 (2013). https://doi.org/10.1016/j.robot.2012.11.005
Blender Foundation: blender.org - home of the blender project - free and open 3D creation software. https://www.blender.org/
Günther, M., Ruiz-Sarmiento, J.R., Galindo, C., González-Jiménez, J., Hertzberg, J.: Context-aware 3D object anchoring for mobile robots. Robot. Auton. Syst. 110, 12–32 (2018). https://doi.org/10.1016/j.robot.2018.08.016
Harnad, S.: The symbol grounding problem. Phys. D 42, 335–346 (1990)
Magistri, F., Chebrolu, N., Stachniss, C.: Segmentation-based 4D registration of plants point clouds for phenotyping. In: 2020 IEEE/RSJ IROS, pp. 2433–2439. IEEE (2020). https://doi.org/10.1109/IROS45743.2020.9340918
Mason, J., Marthi, B.: An object-based semantic world model for long-term change detection and semantic querying. In: 2012 IEEE/RSJ IROS, pp. 3851–3858 (2012). https://doi.org/10.1109/IROS.2012.6385729
Moreau, D., Pointurier, O., Nicolardot, B., Villerd, J., Colbach, N.: In which cropping systems can residual weeds reduce nitrate leaching and soil erosion? Eur. J. Agron. 119, 126015 (2020). https://doi.org/10.1016/j.eja.2020.126015
Niemeyer, M., Pütz, S., Hertzberg, J.: A spatio-temporal-semantic environment representation for autonomous mobile robots equipped with various sensor systems. In: 2022 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI-2022) (2022). https://doi.org/10.1109/MFI55806.2022.9913873
Oliveira, M., Lim, G.H., Seabra Lopes, L., Kasaei, S.H., Tomé, A., Chauhan, A.: A perceptual memory system for grounding semantic representations in intelligent service robots. In: Proceedings of the IEEE/RSJ IROS, IEEE (2014). https://doi.org/10.1109/IROS.2014.6942861
Partel, V., Charan Kakarla, S., Ampatzidis, Y.: Development and evaluation of a low-cost and smart technology for precision weed management utilizing artificial intelligence. Comput. Electron. Agric. 157, 339–350 (2019). https://doi.org/10.1016/J.COMPAG.2018.12.048
Persson, A., Martires, P.Z.D., Loutfi, A., De Raedt, L.: Semantic relational object tracking. IEEE Trans. Cogn. Dev. Syst. 12(1), 84–97 (2020). https://doi.org/10.1109/TCDS.2019.2915763, arXiv:1902.09937 [cs]
Reid, D.: An algorithm for tracking multiple targets. IEEE Trans. Automat. Contr. 24(6), 843–854 (1979). https://doi.org/10.1109/TAC.1979.1102177
Renz, M., Niemeyer, M., Hertzberg, J.: Towards model-based automation of plant-specific weed regulation. 43. GIL-Jahrestagung, Resiliente Agri-Food-Systeme (2023)
Storkey, J., Westbury, D.B.: Managing arable weeds for biodiversity. Pest Manage. Sci. 63(6), 517–523 (2007). https://doi.org/10.1002/PS.1375
Yang, X., et al.: A survey on smart agriculture: development modes, technologies, and security and privacy challenges. IEEE/CAA J. Autom. Sinica 8(2), 273–302 (2021). https://doi.org/10.1109/JAS.2020.1003536
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Niemeyer, M., Renz, M., Hertzberg, J. (2023). Object Anchoring for Autonomous Robots Using the Spatio-Temporal-Semantic Environment Representation SEEREP. In: Seipel, D., Steen, A. (eds) KI 2023: Advances in Artificial Intelligence. KI 2023. Lecture Notes in Computer Science(), vol 14236. Springer, Cham. https://doi.org/10.1007/978-3-031-42608-7_13
Download citation
DOI: https://doi.org/10.1007/978-3-031-42608-7_13
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-42607-0
Online ISBN: 978-3-031-42608-7
eBook Packages: Computer ScienceComputer Science (R0)