Robotic applications involving people often require advanced perception systems to better understand complex real-world scenarios. To address this challenge, photo-realistic and physics simulators are gaining popularity as a means of generating accurate data labeling and designing scenarios for evaluating generalization capabilities, e.g., lighting changes, camera movements or different weather conditions. We develop a photo-realistic framework built on Unreal Engine and AirSim to generate easily scenarios with pedestrians and mobile robots. The framework is capable to generate random and customized trajectories for each person and provides up to 50 ready-to-use people models along with an API for their metadata retrieval. We demonstrate the usefulness of the proposed framework with a use case of multi-target tracking, a popular problem in real pedestrian scenarios. The notable feature variability in the obtained perception data is presented and evaluated.

翻译：涉及人类活动的机器人应用往往需要先进的感知系统以更深入地理解复杂真实场景。为此，基于照片级真实与物理仿真技术生成精确标注数据、设计评估泛化能力（如光照变化、相机运动或不同天气条件）的场景方法日益流行。本文基于Unreal Engine和AirSim构建了一套照片级真实框架，可便捷生成包含行人和移动机器人的场景。该框架能为每个行人生成随机及自定义轨迹，提供多达50个可即用的人体模型及其元数据检索API。我们以多目标跟踪（真实行人场景中的典型问题）为例验证了该框架的实用性，并对所获感知数据中显著的要素可变性进行了分析与评估。