Autonomous Robot Visual-Only Guidance in Agriculture Using Vanishing Point Estimation

Abstract

Autonomous navigation in agriculture is very challenging as it usually takes place outdoors where there is rough terrain, uncontrolled natural lighting, constantly changing organic scenarios and sometimes the absence of a Global Navigation Satellite System (GNSS). In this work, a single camera and a Google coral dev Board Edge Tensor Processing Unit (TPU) setup is proposed to navigate among a woody crop, more specifically a vineyard. The guidance is provided by estimating the vanishing point and observing its position with respect to the central frame, and correcting the steering angle accordingly. The vanishing point is estimated by object detection using Deep Learning (DL) based Neural Networks (NN) to obtain the position of the trunks in the image. The NN’s were trained using Transfer Learning (TL), which requires a smaller dataset than conventional training methods. For this purpose, a dataset with 4221 images was created considering image collection, annotation and augmentation procedures. Results show that our framework can detect the vanishing point with an average of the absolute error of 0.52$^\circ$ and can be considered for autonomous steering.