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，始终需要涵盖传感器、移动车辆及其周围环境的基于模型仿真的广泛仿真框架。为此，模块化、实时且开源的AirSim框架作为社区构建的热门系统，部分满足了这些需求。然而，该框架需要扩展系统以服务于复杂的工业应用，包括设计测试新型传感器模态、同时定位与地图构建（SLAM）、自主导航算法以及基于机器学习模型的迁移学习。本文探讨了我们对开源版AirSim仿真框架的修改与扩展，涉及新型传感器模态、车辆类型，以及通过程序化生成含可变对象的逼真环境的方法。此外，我们展示了该框架可服务的多种应用场景与用例。