  Autonomous navigation is crucial for various robotics applications in
agriculture. However, many existing methods depend on RTK-GPS systems, which
are expensive and susceptible to poor signal coverage. This paper introduces a
state-of-the-art LiDAR-based navigation system that can achieve over-canopy
autonomous navigation in row-crop fields, even when the canopy fully blocks the
interrow spacing. Our crop row detection algorithm can detect crop rows across
diverse scenarios, encompassing various crop types, growth stages, weeds
presence, and discontinuities within the crop rows. Without utilizing the
global localization of the robot, our navigation system can perform autonomous
navigation in these challenging scenarios, detect the end of the crop rows, and
navigate to the next crop row autonomously, providing a crop-agnostic approach
to navigate the whole row-crop field. This navigation system has undergone
tests in various simulated agricultural fields, achieving an average of 2.98cm
autonomous driving accuracy without human intervention on the custom Amiga
robot. In addition, the qualitative results of our crop row detection algorithm
from the actual soybean fields validate our LiDAR-based crop row detection
algorithm's potential for practical agricultural applications.
