Visual-Inertial SLAM for Agricultural Robotics: Benchmarking the Benefits and Computational Costs of Loop Closing
<|reference_start|>Visual-Inertial SLAM for Agricultural Robotics: Benchmarking the Benefits and Computational Costs of Loop Closing: Simultaneous Localization and Mapping (SLAM) is essential for mobile robotics, enabling autonomous navigation in dynamic, unstructured outdoor environments without relying on external positioning systems. In agricultural applications, where environmental conditions can be particularly challenging due to variable lighting or weather conditions, Visual-Inertial SLAM has emerged as a potential solution. This paper benchmarks several open-source Visual-Inertial SLAM systems, including ORB-SLAM3, VINS-Fusion, OpenVINS, Kimera, and SVO Pro, to evaluate their performance in agricultural settings. We focus on the impact of loop closing on localization accuracy and computational demands, providing a comprehensive analysis of these systems' effectiveness in real-world environments and especially their application to embedded systems in agricultural robotics. Our contributions further include an assessment of varying frame rates on localization accuracy and computational load. The findings highlight the importance of loop closing in improving localization accuracy while managing computational resources efficiently, offering valuable insights for optimizing Visual-Inertial SLAM systems for practical outdoor applications in mobile robotics.<|reference_end|>
@article{schmidt2024visual-inertial,
    title={Visual-Inertial SLAM for Agricultural Robotics: Benchmarking the
  Benefits and Computational Costs of Loop Closing},
    author={Fabian Schmidt, Constantin Blessing, Markus Enzweiler, Abhinav Valada},
    journal={arXiv preprint arXiv:2408.01716},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.01716},
    primaryClass={cs.RO cs.CV}
}