Least Squares Optimization: from Theory to Practice
This work addresses optimization challenges in robotics and computer vision, but it appears incremental as it builds on existing solvers and focuses on domain-specific structures.
The authors tackled the problem of designing efficient least-squares optimization algorithms for robotics and computer vision by proposing a unified methodology and a novel open-source system, achieving state-of-the-art performances in all tested scenarios.
Nowadays, Non-Linear Least-Squares embodies the foundation of many Robotics and Computer Vision systems. The research community deeply investigated this topic in the last 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 transparently problems with a different structure and designed to be easy to extend. The system is written in modern C++ and can run efficiently on embedded systems. Source code: https://srrg.gitlab.io/srrg2-solver.html. We 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.