论文标题
最小二乘优化：从理论到实践
Least Squares Optimization: from Theory to Practice
论文作者
论文摘要
如今，非线性最小二乘体现了许多机器人和计算机视觉系统的基础。研究界在过去几年中深入研究了该主题，这导致了几个开源解决者的发展，以不断增加问题类别。在这项工作中，我们提出了一种统一的方法，以设计和开发有效的最小二乘优化算法，重点关注每个特定领域的结构和模式。此外，我们提出了一种新颖的开源优化系统，该系统解决了具有不同结构的透明问题，并旨在易于扩展。该系统用现代C ++编写，可以在嵌入式系统上有效运行。源代码：https：//srrg.gitlab.io/srrg2-solver.html。我们通过使用标准数据集对几个问题进行比较实验来验证我们的方法。结果表明，我们的系统在所有测试的方案中都可以实现最先进的性能。
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.
