Learning-based solutions have enabled incredible capabilities for autonomous systems. Autonomous vehicles, both aerial and ground, rely on DNN for various integral tasks, including perception. The efficacy of supervised learning solutions hinges on the quality of the training data. Discrepancies between training data and operating conditions result in faults that can lead to catastrophic incidents. However, collecting vast amounts of context-sensitive data, with broad coverage of possible operating environments, is prohibitively difficult. Synthetic data generation techniques for DNN allow for the easy exploration of diverse scenarios. However, synthetic data generation solutions for aerial vehicles are still lacking. This work presents a data augmentation framework for aerial vehicle's perception training, leveraging photorealistic simulation integrated with high-fidelity vehicle dynamics. Safe landing is a crucial challenge in the development of autonomous air taxis, therefore, landing maneuver is chosen as the focus of this work. With repeated simulations of landing in varying scenarios we assess the landing performance of the VTOL type UAV and gather valuable data. The landing performance is used as the objective function to optimize the DNN through retraining. Given the high computational cost of DNN retraining, we incorporated Bayesian Optimization in our framework that systematically explores the data augmentation parameter space to retrain the best-performing models. The framework allowed us to identify high-performing data augmentation parameters that are consistently effective across different landing scenarios. Utilizing the capabilities of this data augmentation framework, we obtained a robust perception model. The model consistently improved the perception-based landing success rate by at least 20% under different lighting and weather conditions.

翻译：基于学习的解决方案为自主系统赋予了卓越的能力。自主飞行器与地面车辆均依赖深度神经网络（DNN）执行包括感知在内的多项核心任务。监督学习方法的有效性取决于训练数据的质量。训练数据与运行条件之间的差异会导致故障，可能引发灾难性事故。然而，收集大量覆盖广泛可能运行环境的上下文敏感数据极其困难。面向深度神经网络的合成数据生成技术便于探索多样化场景，但目前仍缺乏针对飞行器的专用合成数据生成方案。本研究提出了一种用于飞行器感知训练的数据增强框架，该框架融合了高保真车辆动力学与逼真视觉模拟。安全着陆是开发自主空中出租车的关键挑战，因此本文以着陆机动作为研究重点。通过对不同场景下的着陆过程进行重复模拟，我们评估了垂直起降（VTOL）型无人机的着陆性能并收集了宝贵数据。着陆性能被用作目标函数，通过重新训练来优化深度神经网络。鉴于深度神经网络再训练的高计算成本，我们在框架中引入了贝叶斯优化方法，以系统探索数据增强参数空间，从而训练出性能最佳的模型。该框架使我们能够识别出在不同着陆场景中均持续有效的高性能数据增强参数。利用此数据增强框架的能力，我们获得了一个鲁棒的感知模型。该模型在不同光照与天气条件下，将持续提升基于感知的着陆成功率至少20%。