An Effective Multi-Cue Positioning System for Agricultural Robotics

TL;DR

This paper introduces a robust multi-sensor positioning system for agricultural robots that significantly improves localization accuracy by integrating diverse data sources and modeling the problem as a pose graph optimization.

Contribution

It presents a novel 3D global pose estimation framework that combines heterogeneous sensors, digital elevation models, and Markov Random Fields to enhance localization in farming environments.

Findings

Achieved 37% to 76% accuracy improvement over GPS-only methods.

Effectively mitigated GPS mode changes and sensor noise.

Demonstrated robustness across various sensor configurations.

Abstract

The self-localization capability is a crucial component for Unmanned Ground Vehicles (UGV) in farming applications. Approaches based solely on visual cues or on low-cost GPS are easily prone to fail in such scenarios. In this paper, we present a robust and accurate 3D global pose estimation framework, designed to take full advantage of heterogeneous sensory data. By modeling the pose estimation problem as a pose graph optimization, our approach simultaneously mitigates the cumulative drift introduced by motion estimation systems (wheel odometry, visual odometry, ...), and the noise introduced by raw GPS readings. Along with a suitable motion model, our system also integrates two additional types of constraints: (i) a Digital Elevation Model and (ii) a Markov Random Field assumption. We demonstrate how using these additional cues substantially reduces the error along the altitude axis and, moreover, how this benefit spreads to the other components of the state. We report exhaustive experiments combining several sensor setups, showing accuracy improvements ranging from 37% to 76% with respect to the exclusive use of a GPS sensor. We show that our approach provides accurate results even if the GPS unexpectedly changes positioning mode. The code of our system along with the acquired datasets are released with this paper.