Multi-Sensor Fusion based Robust Row Following for Compact Agricultural Robots

TL;DR

This paper introduces a LiDAR-based autonomous navigation system for under-canopy agricultural robots that fuses sensor data with an Extended Kalman Filter to achieve robust, intervention-free navigation over long distances in complex crop environments.

Contribution

The paper presents a novel multi-sensor fusion approach combining LiDAR and IMU data with an EKF for robust under-canopy navigation, validated in real-world field conditions.

Findings

Safe navigation without interventions for 386.9 m on average in ideal conditions

Effective navigation with 56.1 m intervention-free distance in production fields

Robust performance across different crop types and field conditions

Abstract

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).