  This paper presents a state-of-the-art LiDAR based autonomous navigation
system for under-canopy agricultural robots. Under-canopy agricultural
navigation has been a challenging problem because GNSS and other positioning
sensors are prone to significant errors due to attentuation and multi-path
caused by crop leaves and stems. Reactive navigation by detecting crop rows
using LiDAR measurements is a better alternative to GPS but suffers from
challenges due to occlusion from leaves under the canopy. Our system addresses
this challenge by fusing IMU and LiDAR measurements using an Extended Kalman
Filter framework on low-cost hardwware. In addition, a local goal generator is
introduced to provide locally optimal reference trajectories to the onboard
controller. Our system is validated extensively in real-world field
environments over a distance of 50.88~km on multiple robots in different field
conditions across different locations. We report state-of-the-art distance
between intervention results, showing that our system is able to safely
navigate without interventions for 386.9~m on average in fields without
significant gaps in the crop rows, 56.1~m in production fields and 47.5~m in
fields with gaps (space of 1~m without plants in both sides of the row).

Robotics