Harnessing the benefits of drones for urban innovation at scale requires
reliable aerial autonomy. One major barrier to advancing aerial autonomy has
been collecting large-scale aerial datasets for training machine learning
models. Due to costly and time-consuming real-world data collection through
deploying drones, there has been an increasing shift towards using synthetic
data for training models in drone applications. However, to increase
generalizability of trained policies on synthetic data, incorporating domain
randomization into the data generation workflow for addressing the sim-to-real
problem becomes crucial. Current synthetic data generation tools either lack
domain randomization or rely heavily on manual workload or real samples for
configuring and generating diverse realistic simulation scenes. These
dependencies limit scalability of the data generation workflow. Accordingly,
there is a major challenge in balancing generalizability and scalability in
synthetic data generation. To address these gaps, we introduce a modular
scalable data generation workflow tailored to aerial autonomy applications. To
generate realistic configurations of simulation scenes while increasing
diversity, we present an adaptive layered domain randomization approach that
creates a type-agnostic distribution space for assets over the base map of the
environments before pose generation for drone trajectory. We leverage
high-level scene structures to automatically place assets in valid
configurations and then extend the diversity through obstacle generation and
global parameter randomization. We demonstrate the effectiveness of our method
in automatically generating diverse configurations and datasets and show its
potential for downstream performance optimization. Our work contributes to
generating enhanced benchmark datasets for training models that can generalize
better to real-world situations.

我们提出了一种可扩展的数据生成流程，以应用于空中自主飞行。该流程利用自适应层次域随机化方法为无人机轨迹生成之前的环境基础地图上的资产创造无类型分布空间，通过障碍物生成和全局参数随机化扩展多样性，以自动产生多样且逼真的模拟场景配置和数据集，以便更好地推广到真实情况下的模型。