When autonomous systems are deployed in real-world scenarios, sensors are often subject to limited field-of-view (FOV) constraints, either naturally through system design, or through unexpected occlusions or sensor failures. In conditions where a large FOV is unavailable, it is important to be able to infer information about the environment and predict the state of nearby surroundings based on available data to maintain safe and accurate operation. In this work, we explore the effectiveness of deep learning for dynamic map state prediction based on limited FOV time series data. We show that by representing dynamic sensor data in a simple single-image format that captures both spatial and temporal information, we can effectively use a wide variety of existing image-to-image learning models to predict map states with high accuracy in a diverse set of sensing scenarios.

翻译：当自主系统部署于真实场景时，传感器常受到有限视场(FOV)约束，这种限制或因系统设计自然产生，或因意外遮挡或传感器故障所致。在无法获得大视场的情况下，基于可用数据推断环境信息并预测周围环境状态，对于维持安全与精确运行至关重要。本研究探索了基于有限视场时序数据的深度学习在动态地图状态预测中的有效性。我们证明：通过将动态传感器数据表示为同时捕获空间与时间信息的简洁单图像格式，可有效利用多种现有图像到图像学习模型，在多样化的传感场景中以高精度预测地图状态。