  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.
