Work

Robust Moving Horizon Estimation for Autonomous Agricultural Vehicles With GNSS Outliers Using a Robust Loss Function

Year: 2025

Type: article

Abstract: We propose a Moving Horizon Estimator (MHE) for autonomous agricultural vehicles to handle GNSS outliers, a common issue in farming. To improve robustness, we replace the standard L2 stage cost with a loss function based on the square of the derivative of the General Adaptive Robust Loss (GARL). The GARL framework, controlled by parameters α ∈ [1, , 2) and c > 0, balances between quadratic and outlier-resistant behavior. By using the derivative, we avoid singularities at α = 0 and α = 2, simplifying tuning and ensuring stable optimization within MHE. This approach retains the flexibility of GARL while narrowing the design space to a singularity-free regime. We prove robust stability under standard assumptions. Simulations show our method outperforms L2-based MHE and state-of-theart methods, rejecting GNSS outliers. Field experiments validate its practical effectiveness.

Source: IEEE Robotics and Automation Letters

DOI: 10.1109/lra.2025.3606377

Authors: Nestor N. Deniz, Guido M. Sanchez, Fernando A. Auat Cheein, Leonardo L. Giovanini

Institutions: Consejo Nacional de Investigaciones Científicas y Técnicas, Universidad Nacional del Litoral, Harper Adams University

Language: English

Topic: Robotics and Sensor-Based Localization

Subfield: Aerospace Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,645

Funder: Consejo Nacional de Investigaciones Científicas y Técnicas