Addressing the challenges of loop detection in agricultural environments
This addresses the challenge of globally consistent mapping and long-term localization in open-field agricultural settings, which is an incremental improvement over existing methods.
The paper tackles the problem of robust loop detection in agricultural environments, achieving a median error of 15cm with a method based on local feature search and stereo geometric refinement.
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.