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：在学术界和工业界中，亟需一种能够包含移动车辆、传感器及其周围环境基于模型仿真的规模化仿真框架。为此，模块化、实时且开源的AirSim框架作为一种社区构建的流行系统，部分满足了这些需求。然而，该框架需要增加系统组件以支持某些复杂工业应用，包括新型传感器模态的设计与测试、同步定位与建图(SLAM)、自主导航算法以及基于机器学习模型的迁移学习。本文讨论了对我们开源版AirSim仿真框架的修改与扩展，涵盖新型传感器模态、车辆类型，以及通过过程化生成具有可更换对象的逼真环境的方法。此外，我们展示了该框架可支持的各种应用场景与使用案例。