This paper introduces WOLF, a C++ estimation framework based on factor graphs and targeted at mobile robotics. WOLF can be used beyond SLAM to handle self-calibration, model identification, or the observation of dynamic quantities other than localization. The architecture of WOLF allows for a modular yet tightly-coupled estimator. Modularity is enhanced via reusable plugins that are loaded at runtime depending on application setup. This setup is achieved conveniently through YAML files, allowing users to configure a wide range of applications without the need of writing or compiling code. Most procedures are coded as abstract algorithms in base classes with varying levels of specialization. Overall, all these assets allow for coherent processing and favor code re-usability and scalability. WOLF can be used with ROS, and is made publicly available and open to collaboration.
翻译:本文介绍了基于要素图和针对移动机器人的C++估计框架 -- -- WORLF。WOLF可以在SLAMM之外用于处理自我校准、模型识别或观测除本地化以外的动态数量。WOLF的架构允许模块化但紧密结合的估量器。通过根据应用程序设置在运行时安装的可重复使用的插件来增强模块性。这一设置通过YAML文件来方便地实现,使用户能够配置范围广泛的应用程序,而无需写入或编译代码。大多数程序被编码为具有不同专业水平的基类的抽象算法。总的来说,所有这些资产都允许进行一致的处理,有利于代码的可重新使用性和可缩放性。WOLF可以与ROS一起使用,并向公众开放,供合作使用。