Adaptive and Efficient Robot Positioning Through Model and Task Fusion

该项目旨在创建适合目的的定位系统，不断适应多样化和不断变化的环境。该项目预计将有助于机器人，计算机视觉和神经形态计算的知识。该项目的预期成果包括突破性的地点识别技术，这些技术解决了最先进技术中的两个基本限制：持续适应，这在安全关键系统中至关重要，以及能源效率，这在资源有限的系统中至关重要。这将带来巨大的好处，例如加速移动的机器人、无人机和增强现实解决方案在制造业、国防、医疗保健、家庭和太空中的部署。

This project aims to create fit-for-purpose positioning systems that continuously adapt to diverse and changing environments. The project expects to contribute to the knowledge across robotics, computer vision, and neuromorphic computing. Expected outcomes of this project include ground-breaking place recognition techniques that address two fundamental limitations in the state-of-the-art: continuous adaptation, critically important in safety-critical systems, and energy efficiency, critically important in resource-constrained systems. This should provide significant benefits, such as accelerated deployment of mobile robots, drones and augmented reality solutions in manufacturing, defence, healthcare, household, and space.