IDEAS home Printed from https://ideas.repec.org/a/gam/jagris/v15y2025i8p872-d1636156.html
   My bibliography  Save this article

Stereo Visual Odometry and Real-Time Appearance-Based SLAM for Mapping and Localization in Indoor and Outdoor Orchard Environments

Author

Listed:
  • Imran Hussain

    (Interdisciplinary Program in Smart Agriculture, College of Agriculture and Life Sciences, Kangwon National University, Chuncheon 24341, Republic of Korea)

  • Xiongzhe Han

    (Interdisciplinary Program in Smart Agriculture, College of Agriculture and Life Sciences, Kangwon National University, Chuncheon 24341, Republic of Korea
    Department of Biosystems Engineering, College of Agriculture and Life Sciences, Kangwon National University, Chuncheon 24341, Republic of Korea)

  • Jong-Woo Ha

    (HADA Co., Ltd., 329-34 Eungi-gil, Iksan-si 54569, Republic of Korea)

Abstract

Agricultural robots can mitigate labor shortages and advance precision farming. However, the dense vegetation canopies and uneven terrain in orchard environments reduce the reliability of traditional GPS-based localization, thereby reducing navigation accuracy and making autonomous navigation challenging. Moreover, inefficient path planning and an increased risk of collisions affect the robot’s ability to perform tasks such as fruit harvesting, spraying, and monitoring. To address these limitations, this study integrated stereo visual odometry with real-time appearance-based mapping (RTAB-Map)-based simultaneous localization and mapping (SLAM) to improve mapping and localization in both indoor and outdoor orchard settings. The proposed system leverages stereo image pairs for precise depth estimation while utilizing RTAB-Map’s graph-based SLAM framework with loop-closure detection to ensure global map consistency. In addition, an incorporated inertial measurement unit (IMU) enhances pose estimation, thereby improving localization accuracy. Substantial improvements in both mapping and localization performance over the traditional approach were demonstrated, with an average error of 0.018 m against the ground truth for outdoor mapping and a consistent average error of 0.03 m for indoor trails with a 20.7% reduction in visual odometry trajectory deviation compared to traditional methods. Localization performance remained robust across diverse conditions, with a low RMSE of 0.207 m. Our approach provides critical insights into developing more reliable autonomous navigation systems for agricultural robots.

Suggested Citation

  • Imran Hussain & Xiongzhe Han & Jong-Woo Ha, 2025. "Stereo Visual Odometry and Real-Time Appearance-Based SLAM for Mapping and Localization in Indoor and Outdoor Orchard Environments," Agriculture, MDPI, vol. 15(8), pages 1-26, April.
    • Handle: RePEc:gam:jagris:v:15:y:2025:i:8:p:872-:d:1636156
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2077-0472/15/8/872/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2077-0472/15/8/872/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. E. M. B. M. Karunathilake & Anh Tuan Le & Seong Heo & Yong Suk Chung & Sheikh Mansoor, 2023. "The Path to Smart Farming: Innovations and Opportunities in Precision Agriculture," Agriculture, MDPI, vol. 13(8), pages 1-26, August.
    2. Suprava Chakraborty & Devaraj Elangovan & Padma Lakshmi Govindarajan & Mohamed F. ELnaggar & Mohammed M. Alrashed & Salah Kamel, 2022. "A Comprehensive Review of Path Planning for Agricultural Ground Robots," Sustainability, MDPI, vol. 14(15), pages 1-19, July.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Anastasios Michailidis & Chrysanthi Charatsari & Thomas Bournaris & Efstratios Loizou & Aikaterini Paltaki & Dimitra Lazaridou & Evagelos D. Lioutas, 2024. "A First View on the Competencies and Training Needs of Farmers Working with and Researchers Working on Precision Agriculture Technologies," Agriculture, MDPI, vol. 14(1), pages 1-12, January.
    2. Kaidong Lei & Bugao Li & Shan Zhong & Hua Yang & Hao Wang & Xiangfang Tang & Benhai Xiong, 2025. "Research on Video Behavior Detection and Analysis Model for Sow Estrus Cycle Based on Deep Learning," Agriculture, MDPI, vol. 15(9), pages 1-13, April.
    3. Luciana Di Gregorio & Lorenzo Nolfi & Arianna Latini & Nikolaos Nikoloudakis & Nils Bunnefeld & Maurizio Notarfonso & Roberta Bernini & Ioannis Manikas & Annamaria Bevivino, 2024. "Getting (ECO)Ready: Does EU Legislation Integrate Up-to-Date Scientific Data for Food Security and Biodiversity Preservation Under Climate Change?," Sustainability, MDPI, vol. 16(23), pages 1-21, December.
    4. Jeannette Aduhene-Chinbuah & Clement Oppong Peprah, 2024. "Multi-risk management in Ghana's agricultural sector: Strategies, actors, and conceptual shifts—a review," Review of Agricultural, Food and Environmental Studies, Springer, vol. 105(4), pages 393-418, December.
    5. Sergio Monteleone & Edmilson Alves de Moraes & Roberto Max Protil & Brenno Tondato de Faria & Rodrigo Filev Maia, 2024. "Proposal of a Model of Irrigation Operations Management for Exploring the Factors That Can Affect the Adoption of Precision Agriculture in the Context of Agriculture 4.0," Agriculture, MDPI, vol. 14(1), pages 1-33, January.
    6. Thana Sarttra & Tossapol Kiatcharoenpol, 2025. "Enhancing Sustainable Herd Structure Management in Thai Dairy Cooperatives Through Dynamic Programming Optimization," Sustainability, MDPI, vol. 17(9), pages 1-23, April.
    7. Deniz Uztürk & Gülçin Büyüközkan, 2023. "Strategic Analysis for Advancing Smart Agriculture with the Analytic SWOT/PESTLE Framework: A Case for Turkey," Agriculture, MDPI, vol. 13(12), pages 1-25, December.
    8. Jung-Kyu Lee & Ye-Hun Lee & Dong-Hoon Lee, 2024. "Proximal Absorbance Calibration Method Using an Embedded Blank Reference RGB Sensor for Determination of Ion Concentrations," Agriculture, MDPI, vol. 14(12), pages 1-16, November.
    9. Cristiano Fragassa & Giuliano Vitali & Luis Emmi & Marco Arru, 2023. "A New Procedure for Combining UAV-Based Imagery and Machine Learning in Precision Agriculture," Sustainability, MDPI, vol. 15(2), pages 1-25, January.
    10. Anca Antoaneta Vărzaru, 2025. "Digital Revolution in Agriculture: Using Predictive Models to Enhance Agricultural Performance Through Digital Technology," Agriculture, MDPI, vol. 15(3), pages 1-31, January.
    11. Tyler Parsons & Fattah Hanafi Sheikhha & Omid Ahmadi Khiyavi & Jaho Seo & Wongun Kim & Sangdae Lee, 2022. "Optimal Path Generation with Obstacle Avoidance and Subfield Connection for an Autonomous Tractor," Agriculture, MDPI, vol. 13(1), pages 1-16, December.
    12. Luca Preite & Federico Solari & Giuseppe Vignali, 2025. "Water Management Optimization in Agriculture: a Digital Model Development," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 39(3), pages 1261-1279, February.
    13. Haoming Shi & Fei Xu & Jinfu Cheng & Victor Shi, 2023. "Exploring the Evolution of the Food Chain under Environmental Pollution with Mathematical Modeling and Numerical Simulation," Sustainability, MDPI, vol. 15(13), pages 1-17, June.
    14. Zanele Adams & Albert Thembinkosi Modi & Simon Kamande Kuria, 2025. "Multidimensional Perspective of Sustainable Agroecosystems and the Impact on Crop Production: A Review," Agriculture, MDPI, vol. 15(6), pages 1-26, March.
    15. André Fonseca & José Cruz & Helder Fraga & Cristina Andrade & Joana Valente & Fernando Alves & Ana Carina Neto & Rui Flores & João A. Santos, 2024. "Vineyard Microclimatic Zoning as a Tool to Promote Sustainable Viticulture under Climate Change," Sustainability, MDPI, vol. 16(8), pages 1-21, April.
    16. Tengfei Zhang & Jinhao Zhou & Wei Liu & Rencai Yue & Jiawei Shi & Chunjian Zhou & Jianping Hu, 2024. "SN-CNN: A Lightweight and Accurate Line Extraction Algorithm for Seedling Navigation in Ridge-Planted Vegetables," Agriculture, MDPI, vol. 14(9), pages 1-20, August.
    17. Chrysanthi Charatsari & Anastasios Michailidis & Evagelos D. Lioutas & Thomas Bournaris & Efstratios Loizou & Aikaterini Paltaki & Dimitra Lazaridou, 2023. "Competencies Needed for Guiding the Digital Transition of Agriculture: Are Future Advisors Well-Equipped?," Sustainability, MDPI, vol. 15(22), pages 1-15, November.
    18. Chenchen Song & Zhiling Guo & Xiaoyue Ma & Jijiang He & Zhengguang Liu, 2025. "Evaluating the Role of Next-Generation Productive Forces in Mitigating Carbon Lock-In: Evidence from Regional Disparities in China," Sustainability, MDPI, vol. 17(9), pages 1-30, May.
    19. Biedny, Christina & Whitacre, Brian E. & Van Leuven, Andrew J., 2024. "Do Gigabits Mean Business? “Ultra-Fast” broadband availability's effect on business births," Information Economics and Policy, Elsevier, vol. 68(C).
    20. Songyu Jiang & Jie Gao & Jing Chen & Xingqi Zhao, 2025. "How Does Digital Inclusive Finance Affect Agricultural Productive Services?—An Economic Model in the Chinese Context," Sustainability, MDPI, vol. 17(2), pages 1-19, January.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jagris:v:15:y:2025:i:8:p:872-:d:1636156. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.