Multi-Sensor Fusion based Robust Row Following for Compact Agricultural Robots
This addresses the challenge of robust row-following for compact agricultural robots in real-world field conditions, representing a strong domain-specific advancement.
The paper tackles the problem of under-canopy agricultural robot navigation by developing a LiDAR-based system that fuses IMU and LiDAR measurements using an Extended Kalman Filter, achieving state-of-the-art results with average intervention-free distances of 386.9 m in fields without gaps, 56.1 m in production fields, and 47.5 m in fields with gaps.
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).