FutureMapping: The Computational Structure of Spatial AI Systems
Andrew J. Davison Department of Computing, Imperial College London, UK
Abstract
We discuss and predict the evolution of Simultaneous Localisation and Mapping (SLAM) into a general geometric and semantic ‘Spatial AI’ perception capability for intelligent embodied devices. A big gap remains between the visual perception performance that devices such as augmented reality eyewear or comsumer robots will require and what is possible within the constraints imposed by real products. Co-design of algorithms, processors and sensors will be needed. We explore the computational structure of current and future Spatial AI algorithms and consider this within the landscape of ongoing hardware developments.
中文速览
让智能设备真正"看懂"并记住周围世界，是当前增强现实眼镜、家用机器人等产品面临的核心难题——它们既要实时理解三维空间的几何与语义，又必须在体积小、功耗低、成本受限的真实产品条件下运行。作者以SLAM（即时定位与地图构建）为出发点，提出其正在向更广义的"空间人工智能"（Spatial AI）演进：系统需要持续构建一个兼具度量几何精度和语义理解能力、可被人类理解的持久世界模型，并将机器学习与显式几何估计有机结合。论文系统梳理了这类算法的计算结构——包括闭环地图更新、数据关联、跟踪与语义融合——并结合GPU、专用AI芯片、事件相机等硬件发展趋势，预测了协同设计算法与处理器的路径。这项工作的重要性在于，它为学术界和工业界指明了一条在严苛资源约束下实现真正实用空间智能的研究方向，对未来消费级机器人和可穿戴AR设备的落地具有直接指导意义。
原文 arXiv:1803.11288;中英对照 + 大白话阅读 https://aha.fim.ai/paper/1803.11288v1