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Open AccessArticle

Least Squares Optimization: From Theory to Practice

Department of Computer, Control, and Management Engineering “Antonio Ruberti”, Sapienza University of Rome, 00185 Rome, Italy
Robot Navigation and Perception (CR/AER1), Robert Bosch Corporate Research, 70839 Stuttgart, Germany
Author to whom correspondence should be addressed.
Robotics 2020, 9(3), 51;
Received: 25 May 2020 / Revised: 20 June 2020 / Accepted: 27 June 2020 / Published: 1 July 2020
(This article belongs to the Section Industrial Robots & Automation)
Nowadays, Nonlinear Least-Squares embodies the foundation of many Robotics and Computer Vision systems. The research community deeply investigated this topic in the last few years, and this resulted in the development of several open-source solvers to approach constantly increasing classes of problems. In this work, we propose a unified methodology to design and develop efficient Least-Squares Optimization algorithms, focusing on the structures and patterns of each specific domain. Furthermore, we present a novel open-source optimization system that addresses problems transparently with a different structure and designed to be easy to extend. The system is written in modern C++ and runs efficiently on embedded systemsWe validated our approach by conducting comparative experiments on several problems using standard datasets. The results show that our system achieves state-of-the-art performances in all tested scenarios.
Keywords: nonlinear optimization; SLAM; mapping; tutorial nonlinear optimization; SLAM; mapping; tutorial
MDPI and ACS Style

Grisetti, G.; Guadagnino, T.; Aloise, I.; Colosi, M.; Della Corte, B.; Schlegel, D. Least Squares Optimization: From Theory to Practice. Robotics 2020, 9, 51.

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