Mapping the surrounding environment is essential for the successful operation of autonomous robots. While extensive research has focused on mapping geometric structures and static objects, the environment is also influenced by the movement of dynamic objects. Incorporating information about spatial motion patterns can allow mobile robots to navigate and operate successfully in populated areas. In this paper, we propose a deep state-space model that learns the map representations of spatial motion patterns and how they change over time at a certain place. To evaluate our methods, we use two different datasets: one generated dataset with specific motion patterns and another with real-world pedestrian data. We test the performance of our model by evaluating its learning ability, mapping quality, and application to downstream tasks. The results demonstrate that our model can effectively learn the corresponding motion pattern, and has the potential to be applied to robotic application tasks.

翻译：映射周围环境对于自主机器人的成功运行至关重要。尽管大量研究聚焦于几何结构和静态物体的映射，但环境同样受到动态物体运动的影响。整合空间运动模式的信息能使移动机器人在有人区域成功导航和运作。本文提出一种深度状态空间模型，该模型学习空间运动模式的地图表示及其在特定地点随时间的变化规律。为评估所提方法，我们使用两种不同数据集：一是具有特定运动模式的生成数据集，二是真实世界的行人数据集。我们从学习能力、映射质量及下游任务应用三个方面测试模型性能。结果表明，本模型能有效学习相应的运动模式，并具备应用于机器人任务领域的潜力。