We present LiSTA (LiDAR Spatio-Temporal Analysis), a system to detect probabilistic object-level change over time using multi-mission SLAM. Many applications require such a system, including construction, robotic navigation, long-term autonomy, and environmental monitoring. We focus on the semi-static scenario where objects are added, subtracted, or changed in position over weeks or months. Our system combines multi-mission LiDAR SLAM, volumetric differencing, object instance description, and correspondence grouping using learned descriptors to keep track of an open set of objects. Object correspondences between missions are determined by clustering the object's learned descriptors. We demonstrate our approach using datasets collected in a simulated environment and a real-world dataset captured using a LiDAR system mounted on a quadruped robot monitoring an industrial facility containing static, semi-static, and dynamic objects. Our method demonstrates superior performance in detecting changes in semi-static environments compared to existing methods.

翻译：摘要：我们提出LiSTA（LiDAR时空分析系统），一种利用多任务SLAM检测概率性对象级随时间变化的系统。诸多应用领域需要此类系统，包括建筑施工、机器人导航、长期自主运行以及环境监测。我们聚焦于半静态场景——其中物体在数周或数月内被添加、移除或改变位置。该系统融合了多任务LiDAR SLAM、体素差异分析、物体实例描述以及基于学习描述子的对应分组技术，以追踪一个开放物体集合。通过聚类物体的学习描述子来确定任务间的物体对应关系。我们在模拟环境中采集的数据集以及真实世界数据集上验证了该方法——真实数据集由搭载于四足机器人上的LiDAR系统采集，用于监测包含静态、半静态和动态物体的工业设施。与现有方法相比，我们的方法在半静态环境变化检测中展现出更优性能。