Trunk detection in tree crops using RGB-D images for structure-based ICM-SLAM

Abstract

Agricultural environments with tree plantations usually present a regular structure that can be used by SLAM systems to improve self-location, and therefore, facilitate the autonomous navigation. In this context, tree trunks are natural landmarks that can be used to incorporate the environment structure into the problem modeling. This article presents a trunk detector solely based on RGB-D data obtained from a frontal-view stereo camera, and a SLAM system that incorporates the regular tree distribution of these crops. The trunk detector can be adapted to similar agricultural environments because its parameters have specific geometric meanings, which differentiates it from black box-type procedures. The structure-based SLAM system has theoretical and practical advantages over the well-known SLAM procedures in the mentioned context. This proposal can be executed on-line and is experimentally tested with databases obtained from a challenging agricultural environment. Results show a good performance and robustness when the database is spatially or temporally subsampled, even under adverse lighting conditions.