Abstract
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.
Abstract (translated)
在学术界和工业界，需要有扩展性的模拟框架，其中包括基于模型的传感器、移动车辆及其周围环境的模拟。为此，模块化、实时且开源的AirSim框架已成为一个受欢迎的社区构建系统，满足了其中一些需求。然而，框架需要添加系统以服务一些复杂的工业应用，包括设计和测试新的传感器模式、同时定位和地图(SLAM)、自主导航算法以及与机器学习模型的转移学习。在这项工作中，我们讨论了我们开源版本的AirSim模拟框架的修改和添加，包括新的传感器模式、车辆类型和方法，以生成具有可变化对象的实际环境。此外，我们展示了框架可以服务的多种应用和 use cases。
URL
https://arxiv.org/abs/2303.13381
