Addressing the challenges of loop detection in agricultural environments
<|reference_start|>Addressing the challenges of loop detection in agricultural environments: While visual SLAM systems are well studied and achieve impressive results in indoor and urban settings, natural, outdoor and open-field environments are much less explored and still present relevant research challenges. Visual navigation and local mapping have shown a relatively good performance in open-field environments. However, globally consistent mapping and long-term localization still depend on the robustness of loop detection and closure, for which the literature is scarce. In this work we propose a novel method to pave the way towards robust loop detection in open fields, particularly in agricultural settings, based on local feature search and stereo geometric refinement, with a final stage of relative pose estimation. Our method consistently achieves good loop detections, with a median error of 15cm. We aim to characterize open fields as a novel environment for loop detection, understanding the limitations and problems that arise when dealing with them.<|reference_end|>
@article{soncini2024addressing,
    title={Addressing the challenges of loop detection in agricultural environments},
    author={Nicol'as Soncini, Javier Civera and Taih'u Pire},
    journal={Journal of Field Robotics (2024), 1-10},
    year={2024},
    doi={10.1002/rob.22414},
    archivePrefix={arXiv},
    eprint={2408.15761},
    primaryClass={cs.RO cs.CV}
}