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A Multisensor Factor-Graph SLAM Framework for Steep Slope Vineyards

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Robot 2023: Sixth Iberian Robotics Conference (ROBOT 2023)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 978))

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Abstract

Steep slope vineyards pose specific challenges for autonomous robot navigation, therefore requiring accurate, robust and scalable localization and mapping solutions for such goal. In addition, due to the unevenness of the terrain, the identification of traversable zones is crucial for a safe operation, thus requiring a dense scene representation that captures these details. For such reasons, a novel SLAM architecture is presented in this work, characterized by a multi-sensor based dual factor-graph framework that integrates in real time wheel odometry, IMU, LIDAR and GNSS measurements, as well as heading and attitude data, generating a dense 3D map in point cloud format. The proposed system was tested with datasets obtained from a real robot navigating in vineyards with different levels of steepness, and benchmarked with state-of-the-art 3D LIDAR SLAM techniques. The presented results demonstrate superior performance over the compared methods, while maintaining overall map consistency and accuracy when matched with a reference model.

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Notes

  1. 1.

    https://scorpion-h2020.eu/.

  2. 2.

    https://www.ros.org/.

  3. 3.

    https://github.com/ros2/message_filters.

  4. 4.

    https://zenodo.org/communities/scorpion-h2020/.

  5. 5.

    https://www.novaterraproject.eu/.

  6. 6.

    https://github.com/eperdices/LeGO-LOAM-SR.

  7. 7.

    https://github.com/TixiaoShan/LIO-SAM.

  8. 8.

    https://github.com/hku-mars/FAST_LIO.

  9. 9.

    github.com/MichaelGrupp/evo.

  10. 10.

    https://www.cloudcompare.org/.

References

  1. Aguiar, A.S., dos Santos, F.N., Cunha, J.B., Sobreira, H., Sousa, A.J.: Localization and mapping for robots in agriculture and forestry: a survey. Robotics 9(4), 97 (2020)

    Article  Google Scholar 

  2. Aguiar, A.S., dos Santos, F.N., Sobreira, H., Boaventura-Cunha, J., Sousa, A.J.: Localization and mapping on agriculture based on point-feature extraction and semiplanes segmentation from 3D LiDAR data. Front. Robotics AI 9 (2022)

    Google Scholar 

  3. Cai, Y., Xu, W., Zhang, F.: ikd-tree: an incremental KD tree for robotic applications. arXiv preprint arXiv:2102.10808 (2021)

  4. Ferreira, J., Moreira, A.P., Silva, M., Santos, F.: A survey on localization, mapping, and trajectory planning for quadruped robots in vineyards. In: 2022 IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC). IEEE (2022)

    Google Scholar 

  5. Forster, C., Carlone, L., Dellaert, F., Scaramuzza, D.: IMU preintegration on manifold for efficient visual-inertial maximum-a-posteriori estimation. In: Robotics: Science and Systems XI. Robotics: Science and Systems Foundation (2015)

    Google Scholar 

  6. Hroob, I., Polvara, R., Molina, S., Cielniak, G., Hanheide, M.: Benchmark of visual and 3D lidar SLAM systems in simulation environment for vineyards. In: Fox, C., Gao, J., Ghalamzan Esfahani, A., Saaj, M., Hanheide, M., Parsons, S. (eds.) TAROS 2021. LNCS (LNAI), vol. 13054, pp. 168–177. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-89177-0_17

    Chapter  Google Scholar 

  7. Kaess, M., Johannsson, H., Roberts, R., Ila, V., Leonard, J.J., Dellaert, F.: iSAM2: Incremental smoothing and mapping using the Bayes tree. Int. J. Robotics Res. 31(2), 216–235 (2011)

    Article  Google Scholar 

  8. Karney, C.: Geographiclib (2015). http://geographiclib.sourceforge.net

  9. Koide, K., Miura, J., Menegatti, E.: A portable three-dimensional LIDAR-based system for long-term and wide-area people behavior measurement. Int. J. Adv. Robotic Syst. 16(2), 172988141984,153 (2019)

    Google Scholar 

  10. Labbé, M., Michaud, F.: RTAB-map as an open-source lidar and visual simultaneous localization and mapping library for large-scale and long-term online operation. J. Field Robotics 36(2), 416–446 (2018)

    Article  Google Scholar 

  11. Le, T., Gjevestad, J.G.O., From, P.J.: Online 3D mapping and localization system for agricultural robots. IFAC-PapersOnLine 52(30), 167–172 (2019)

    Article  Google Scholar 

  12. Lowe, T., Moghadam, P., Edwards, E., Williams, J.: Canopy density estimation in perennial horticulture crops using 3D spinning lidar SLAM. J. Field Robotics 38(4), 598–618 (2021)

    Article  Google Scholar 

  13. Mur-Artal, R., Tardos, J.D.: ORB-SLAM2: an open-source SLAM system for monocular, stereo, and RGB-d cameras. IEEE Trans. Rob. 33(5), 1255–1262 (2017)

    Article  Google Scholar 

  14. Nellithimaru, A.K., Kantor, G.A.: ROLS : robust object-level SLAM for grape counting. In: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). IEEE (2019)

    Google Scholar 

  15. Papadimitriou, A., et al.: Loop closure detection and SLAM in vineyards with deep semantic cues. In: 2022 International Conference on Robotics and Automation (ICRA). IEEE (2022)

    Google Scholar 

  16. Sakai, T., Koide, K., Miura, J., Oishi, S.: Large-scale 3D outdoor mapping and on-line localization using 3D-2D matching. In: 2017 IEEE/SICE International Symposium on System Integration (SII). IEEE (2017)

    Google Scholar 

  17. Shan, T., Englot, B.: LeGO-LOAM: lightweight and ground-optimized lidar odometry and mapping on variable terrain. In: 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE (2018)

    Google Scholar 

  18. Shan, T., Englot, B., Meyers, D., Wang, W., Ratti, C., Rus, D.: LIO-SAM: tightly-coupled lidar inertial odometry via smoothing and mapping. In: 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE (2020)

    Google Scholar 

  19. Sparrow, R., Howard, M.: Robots in agriculture: prospects, impacts, ethics, and policy. Precision Agric. 22(3), 818–833 (2020)

    Article  Google Scholar 

  20. Vizzo, I., Guadagnino, T., Mersch, B., Wiesmann, L., Behley, J., Stachniss, C.: KISS-ICP: In defense of point-to-point ICP – simple, accurate, and robust registration if done the right way. IEEE Robotics Autom. Lett. 8(2), 1029–1036 (2023)

    Article  Google Scholar 

  21. Xu, W., Cai, Y., He, D., Lin, J., Zhang, F.: FAST-LIO2: fast direct LiDAR-inertial odometry. IEEE Trans. Rob. 38(4), 2053–2073 (2022)

    Article  Google Scholar 

  22. Zhang, J., Singh, S.: LOAM: Lidar odometry and mapping in real-time. In: Robotics: Science and Systems X. Robotics: Science and Systems Foundation (2014)

    Google Scholar 

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Acknowledgment

This work has been developed under the SCORPION European project and received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement no. 101004085. The authors thank the project partner INESC TEC for granting access to the Agrob E-Modular robot for dataset collection, as well as for providing the 3D model of the Quinta do Seixo vineyard used in this study.

Author information

Authors and Affiliations

  1. Eurecat, Technology Centre of Catalonia, Robotics and Automation Unit, Cerdanyola del Vallès, Spain

    Mateus S. Moura, Xavier Ruiz, Daniel Serrano & Carlos Rizzo

Authors
  1. Mateus S. Moura
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  2. Xavier Ruiz
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  3. Daniel Serrano
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  4. Carlos Rizzo
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Corresponding author

Correspondence to Mateus S. Moura .

Editor information

Editors and Affiliations

  1. Dep. de Eng. Electrotecnica e de Computadores, University of Coimbra, Coimbra, Portugal

    Lino Marques

  2. Department of Electrónica Industrial, University of Minho, Escola de Engenharia, Guimarães, Portugal

    Cristina Santos

  3. Department of Electrical Engineering, ESTIG, Polytechnic Institute of Bragança, Bragança, Portugal

    José Luís Lima

  4. Centro Universitario de la Defensa, Zaragoza, Spain

    Danilo Tardioli

  5. Centre for Automation and Robotics UPM-CSIC, Universidad Politécnica de Madrid, Madrid, Spain

    Manuel Ferre

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Moura, M.S., Ruiz, X., Serrano, D., Rizzo, C. (2024). A Multisensor Factor-Graph SLAM Framework for Steep Slope Vineyards. In: Marques, L., Santos, C., Lima, J.L., Tardioli, D., Ferre, M. (eds) Robot 2023: Sixth Iberian Robotics Conference. ROBOT 2023. Lecture Notes in Networks and Systems, vol 978. Springer, Cham. https://doi.org/10.1007/978-3-031-59167-9_32

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