MAPS

Automating logistics/agrifood vehicles requires dependable, accurate positioning. Automated vehicles, or mobile robots, constantly need to know their exact position to follow the trajectories required to perform their tasks. Precise outdoor localization is helped by the increased price/performance ratio of RTK-GNSS solutions. However, this technology is sensitive to signal deterioration by e.g. biomass and large structures like poles/buildings. Robust localization requires additional localization technologies. Several absolute and relative positioning technologies exist and available sensor fusion solutions allow for combining these technologies. However, robot developers require modularity, and no integral solutions exist. Commercial solutions are either customized or high-priced testing solutions. Academics mainly propose specific sensing combinations and lack industrial applicability. Market demand articulation expresses the need for redundancy besides modularity, both for vehicle safety and system resilience, referring to the current geopolitical GPS jamming reality.
MAPS aims for an open-source, ROS2-based, multi-modal, robust and modular localization solution for outdoor logistics and agrifood applications, enabling dependable and safe vehicle automation, allowing both sectors to handle labor shortages, introduce durable solutions and enhance resilience. MAPS focuses on a sensor fusion approach allowing modularity, with integrated redundancy. It includes online confidence level estimation, supporting both continuous fusion and modality switching, aiming for location/situation aware behavior and allowing for market-requested hybrid in-vehicle/infra solutions. MAPS intents to maximally utilize the consortium’s vehicle dynamics knowledge - including vehicle-(soft)soil interaction - in the solution for plausibility and dead reckoning.
An accompanying PhD/EngD research is foreseen. With project partners enabling scalable, industry-grade solutions MAPS aims to bridge the gap between academic-level research and market-desired applicability.
MAPS is independent, though aims to cooperate with AIFusIOn from Saxion on re-usable architectures and integration of AIFusIOn specifics, like AI-based situational awareness and indoor-outdoor switching, if both are granted.