Within academia and industry, there has been a need for expansive simulation frameworks that include model-based simulation of sensors, mobile vehicles, and the environment around them. To this end, the modular, real-time, and open-source AirSim framework has been a popular community-built system that fulfills some of those needs. However, the framework required adding systems to serve some complex industrial applications, including designing and testing new sensor modalities, Simultaneous Localization And Mapping (SLAM), autonomous navigation algorithms, and transfer learning with machine learning models. In this work, we discuss the modification and additions to our open-source version of the AirSim simulation framework, including new sensor modalities, vehicle types, and methods to generate realistic environments with changeable objects procedurally. Furthermore, we show the various applications and use cases the framework can serve.
翻译:在学术界和工业界,需要一个包括基于模型的传感器仿真、移动车辆仿真和环境仿真的广泛仿真框架。为此,模块化、实时和开源的AirSim框架是一个流行的社区构建系统,满足了部分需求。然而,这个框架需要引入系统来服务一些复杂的工业应用,包括设计和测试新的传感器模态、同时定位和制图(SLAM)、自主导航算法和机器学习模型的迁移学习。在这项工作中,我们讨论了我们的开源版本AirSim仿真框架的修改和添加,包括新的传感器模态、车辆类型和用程序生成可变对象逼真环境的方法。此外,我们展示了该框架可以服务的各种应用和用例。