The capability of a mobile robot to efficiently and safely perform complex missions is limited by its knowledge of the environment, namely the situation. Advanced reasoning, decision-making, and execution skills enable an intelligent agent to act autonomously in unknown environments. Situational Awareness (SA) is a fundamental capability of humans that has been deeply studied in various fields, such as psychology, military, aerospace, and education. Nevertheless, it has yet to be considered in robotics, which has focused on single compartmentalized concepts such as sensing, spatial perception, sensor fusion, state estimation, and Simultaneous Localization and Mapping (SLAM). Hence, the present research aims to connect the broad multidisciplinary existing knowledge to pave the way for a complete SA system for mobile robotics that we deem paramount for autonomy. To this aim, we define the principal components to structure a robotic SA and their area of competence. Accordingly, this paper investigates each aspect of SA, surveying the state-of-the-art robotics algorithms that cover them, and discusses their current limitations. Remarkably, essential aspects of SA are still immature since the current algorithmic development restricts their performance to only specific environments. Nevertheless, Artificial Intelligence (AI), particularly Deep Learning (DL), has brought new methods to bridge the gap that maintains these fields apart from the deployment to real-world scenarios. Furthermore, an opportunity has been discovered to interconnect the vastly fragmented space of robotic comprehension algorithms through the mechanism of Situational Graph (S-Graph), a generalization of the well-known scene graph. Therefore, we finally shape our vision for the future of robotic Situational Awareness by discussing interesting recent research directions.

翻译：移动机器人高效、安全地执行复杂任务的能力受限于其对环境（即情境）的认知水平。高级推理、决策与执行能力使智能体能够在未知环境中自主行动。情境感知是人类的核心能力之一，已在心理学、军事、航空航天和教育等领域得到深入研究。然而，在机器人学中，该能力尚未得到充分重视——现有研究多聚焦于感知、空间认知、传感器融合、状态估计及同时定位与地图构建（SLAM）等单一分散的概念。为此，本研究旨在融合广泛的多学科现有知识，为构建移动机器人完整情境感知系统（我们认为这对自主性至关重要）铺平道路。基于此目标，我们定义了构建机器人情境感知的核心组件及其功能领域。本文系统探究了情境感知的每个方面，梳理了覆盖这些方面的前沿机器人算法，并讨论了当前局限。值得注意的是，由于现有算法性能局限于特定环境，情境感知的关键要素仍不成熟。尽管如此，人工智能（尤其是深度学习）已引入新方法，弥合了算法与现实场景部署之间的鸿沟。此外，通过情境图（S-Graph）机制——一种对经典场景图的泛化——我们发现可将碎片化的机器人理解算法相互关联。最后，我们通过探讨近期有意义的研究方向，阐述了机器人情境感知的未来愿景。