Evaluation of Visual SLAM Algorithms on Agricultural Dataset

ROMÁN COMELLI; TAIHÚ PIRE; ERNESTO KOFMAN

Bahía Blanca

Conferencia; XVIII Reunión de Trabajo en Procesamiento de la Información y Control; 2019

Universidad Nacional del Sur

In the last decade, the interest in using fully autonomous mobile robots for agricultural tasks has been growing significantly. Agricultural environments are highly visual repetitive and present high dynamic scenes because of the movement of the leafs of the field caused by the wind. These features, among others, make the agricultural environment a very strong challenge for vision-based SLAM systems. In this work, we assess the well-known S-PTAM and ORB-SLAM2 Visual SLAM systems and the Visual-Inertial SLAM S-MSCKF in agricultural environments. In particular the evaluation is performed on the recently released Rosario dataset. The evaluation shows that the three systems achieve a poor performance in terms of accuracy and robustness in contrast to the performance reported on urban or indoor environments where they are usually tested.