Autonomous inspection of infrastructure on land and in water is a quickly growing market, with applications including surveying constructions, monitoring plants, and tracking environmental changes in on- and off-shore wind energy farms. For Autonomous Underwater Vehicles and Unmanned Aerial Vehicles overfitting of controllers to simulation conditions fundamentally leads to poor performance in the operation environment. There is a pressing need for more diverse and realistic test data that accurately represents the challenges faced by these systems. We address the challenge of generating perception test data for autonomous systems by leveraging Neural Radiance Fields to generate realistic and diverse test images, and integrating them into a metamorphic testing framework for vision components such as vSLAM and object detection. Our tool, N2R-Tester, allows training models of custom scenes and rendering test images from perturbed positions. An experimental evaluation of N2R-Tester on eight different vision components in AUVs and UAVs demonstrates the efficacy and versatility of the approach.

翻译：陆上与水下基础设施的自主巡检是一个快速发展的市场，其应用包括建筑勘测、工厂监测以及陆上与海上风电场环境变化的追踪。对于自主水下航行器和无人飞行器而言，控制器对仿真条件的过拟合从根本上导致了其在运行环境中的性能不佳。迫切需要更多样化、更真实且能准确反映这些系统所面临挑战的测试数据。我们通过利用神经辐射场生成真实且多样化的测试图像，并将其集成到针对视觉组件（如vSLAM和目标检测）的蜕变测试框架中，以应对为自主系统生成感知测试数据的挑战。我们的工具N2R-Tester支持对自定义场景进行建模训练，并从扰动位置渲染测试图像。在AUV和UAV的八种不同视觉组件上对N2R-Tester进行的实验评估证明了该方法的有效性和通用性。