Crop Row Switching for Vision-Based Navigation: A Comprehensive Approach
  for Efficient Crop Field Navigation
Rajitha de Silva Grzegorz Cielniak Junfeng Gao
Vision-based mobile robot navigation systems in arable fields are mostly
limited to in-row navigation. The process of switching from one crop row to the
next in such systems is often aided by GNSS sensors or multiple camera setups.
This paper presents a novel vision-based crop row-switching algorithm that
enables a mobile robot to navigate an entire field of arable crops using a
single front-mounted camera. The proposed row-switching manoeuvre uses deep
learning-based RGB image segmentation and depth data to detect the end of the
crop row, and re-entry point to the next crop row which would be used in a
multi-state row switching pipeline. Each state of this pipeline use visual
feedback or wheel odometry of the robot to successfully navigate towards the
next crop row. The proposed crop row navigation pipeline was tested in a real
sugar beet field containing crop rows with discontinuities, varying light
levels, shadows and irregular headland surfaces. The robot could successfully
exit from one crop row and re-enter the next crop row using the proposed
pipeline with absolute median errors averaging at 19.25 cm and 6.77{\deg} for
linear and rotational steps of the proposed manoeuvre.