Addressing the challenges of loop detection in agricultural environments

While visual Simultaneous Localization and Mapping 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 15 cm. We aim to characterize open fields as a novel environment for loop detection, understanding the limitations and problems that arise when dealing with them. Code is available at: https://github.com/CIFASIS/StereoLoopDetector