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：学术界与工业界一直需要包含传感器、移动车辆及其周围环境的基于模型仿真的广泛模拟框架。为此，模块化、实时且开源的AirSim框架成为满足部分需求的流行社区构建系统。然而，该框架需要扩展系统以支持复杂工业应用，包括新型传感器模态的设计与测试、同时定位与地图构建（SLAM）、自主导航算法以及机器学习模型的迁移学习。本文论述了我们对开源版AirSim仿真框架的修改与扩展，涵盖新增传感器模态、车辆类型以及通过程序化生成可变物体的逼真环境方法。此外，我们展示了该框架可服务的多种应用场景与用例。