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及变化的环境是机器人自主性的关键能力。尽管在开发能够准确估计机器人位姿的动态SLAM方法方面已取得重大进展，但针对构建机器人环境密集时空表示的关注相对较少。对场景及其随时间演化的详细理解对于长期机器人自主性至关重要，并且是执行需要长期推理的任务（例如在与人类和其他代理共享的环境中有效运行，从而受到短期和长期动态影响）所不可或缺的。为应对这一挑战，本工作定义了时空度量语义SLAM（SMS）问题，并提出一个高效分解并求解该问题的框架。研究表明，所提出的分解方式自然暗示了时空感知系统的组织架构：快速进程在活动时间窗口内追踪短期动态，而慢速进程则利用因子图公式对环境中的长期变化进行推理。我们提供了所提时空感知方法的高效实现，命名为Khronos，并证明其统一了现有对短期与长期动态的诠释，能够实时构建密集的时空地图。通过仿真与真实实验结果，显示Khronos构建的时空地图能随时间精确反映3D场景，且在多项指标上优于基线方法。我们进一步在挑战性大规模真实环境中，基于两台异构机器人验证了该方法的有效性。