Ernesto Kofman

About

Ernesto Kofman is a leading researcher in agricultural robotics, specializing in autonomous navigation and localization for field robots. His work directly addresses the critical need for precise, reliable systems to automate tasks in arable farming, tackling challenges from sensor fusion to efficient path planning. Kofman’s major contributions include the creation of the **Rosario dataset** (2019, 90 citations), a foundational multisensor collection for localization and mapping in agricultural environments that filled a critical gap in realistic field data. He has also advanced **GNSS-stereo-inertial SLAM** for drift-free localization in crop rows and developed novel path-planning algorithms, including a **Travelling Salesman Problem approach** for efficiently navigating long crop rows with car-like robots. His recent work on **stabilizing model predictive control (FCS-MPC)** for precise path following demonstrates his commitment to robust, real-world solutions. Kofman’s research is pivotal for enabling autonomous robots to operate accurately and efficiently in unstructured agricultural settings, directly supporting the push toward sustainable, automated food production.

Research Focus

Key Achievements

Top Papers

1. 1
   The Rosario dataset: Multisensor data for localization and mapping in agricultural environments

   90 citations · 2019
2. 2
   GNSS‐stereo‐inertial SLAM for arable farming

   16 citations · 2023
3. 3
   A Travelling Salesman Problem Approach to Efficiently Navigate Crop Row Fields with a Car-Like Robot

   7 citations · 2023
4. 4
   Application of a stabilizing model predictive controller to path following for a car‐like agricultural robot

   4 citations · 2024

Key Collaborators

Contact & Links