Enhanced Environmental Context Encoding for Accurate Trajectory Prediction in Intralogistics

Trajectory prediction is an essential component of the perception stack in autonomous mobile robots (AMRs). AMRs operate in complex environments where their movements are influenced by various environmental elements, such as racks and storage locations. Therefore, accurate and efficient trajectory prediction for intralogistics requires detailed environment modeling that goes beyond the lane-based context commonly used for road traffic. We propose a new environment context encoder that can be seamlessly integrated into state-of-the-art motion forecasting models. Our approach, tailored to the specific challenges of intralogistics, achieves highly accurate predictions using efficient baseline networks.