Perceiving and understanding highly dynamic and changing environments is a crucial capability for robot autonomy. While large strides have been made towards developing dynamic SLAM approaches that estimate the robot pose accurately, a lesser emphasis has been put on the construction of dense spatio-temporal representations of the robot environment. A detailed understanding of the scene and its evolution through time is crucial for long-term robot autonomy and essential to tasks that require long-term reasoning, such as operating effectively in environments shared with humans and other agents and thus are subject to short and long-term dynamics. To address this challenge, this work defines the Spatio-temporal Metric-semantic SLAM (SMS) problem, and presents a framework to factorize and solve it efficiently. We show that the proposed factorization suggests a natural organization of a spatio-temporal perception system, where a fast process tracks short-term dynamics in an active temporal window, while a slower process reasons over long-term changes in the environment using a factor graph formulation. We provide an efficient implementation of the proposed spatio-temporal perception approach, that we call Khronos, and show that it unifies exiting interpretations of short-term and long-term dynamics and is able to construct a dense spatio-temporal map in real-time. We provide simulated and real results, showing that the spatio-temporal maps built by Khronos are an accurate reflection of a 3D scene over time and that Khronos outperforms baselines across multiple metrics. We further validate our approach on two heterogeneous robots in challenging, large-scale real-world environments.

翻译：感知和理解高度动态且变化的环境是机器人自主性的关键能力。尽管在开发准确估计机器人位姿的动态SLAM方法方面已取得重大进展，但对构建机器人环境的密集时空表示关注较少。对场景及其随时间演变的详细理解对于长期机器人自主性至关重要，并且对于需要长期推理的任务（例如在与人及其他智能体共享的环境中有效运行，从而受短期和长期动态影响）不可或缺。为解决这一挑战，本文定义了时空度量语义SLAM（SMS）问题，并提出一个框架以高效分解和求解该问题。我们表明，所提出的分解方案自然暗示了一种时空感知系统的组织方式：快速过程在活跃时间窗口内跟踪短期动态，而较慢过程通过因子图公式对环境中的长期变化进行推理。我们提供了所提出的时空感知方法的高效实现，称之为Khronos，并证明其统一了现有对短期和长期动态的解释，且能够实时构建密集的时空地图。我们给出了仿真和真实环境中的结果，表明Khronos构建的时空地图能准确反映三维场景随时间的演变，并在多个指标上优于基线方法。我们进一步在两个异构机器人上于具有挑战性的大规模真实环境中验证了该方法。