Uploaded March 2024 | Updated September 2026, 3 weeks ago
ori-drs.github.io/LiSTA_dataset
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.
Accepted to IEEE International Conference on Robotics and Automation (ICRA) 2024 Conference
Authors: Joseph Rowell, Lintong Zhang, Maurice Fallon
Pre-print: arxiv.org/abs/2403.02175
PDF: arxiv.org/pdf/2403.02175.pdf
ori-drs.github.io/LiSTA_dataset
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.
Accepted to IEEE International Conference on Robotics and Automation (ICRA) 2024 Conference
Authors: Joseph Rowell, Lintong Zhang, Maurice Fallon
Pre-print: arxiv.org/abs/2403.02175
PDF: arxiv.org/pdf/2403.02175.pdf
