# LiSTA: Geometric Object-Based Change Detection in Cluttered Environments

arXiv: https://arxiv.org/abs/2403.02175 · HTML: https://mlsanity.com/paper/2403.02175/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Mar 2024.

## Built on by (most cited first, 4 of 4)

- [Gaussian Mapping for Evolving Scenes](https://mlsanity.com/paper/2506.06909/) (2025)
- [LT-Exosense: A Vision-centric Multi-session Mapping System for Lifelong Safe Navigation of Exoskeletons](https://mlsanity.com/paper/2510.22164/) (2025)
- [Chamelion: Reliable Change Detection for Long-Term LiDAR Mapping in Transient Environments](https://mlsanity.com/paper/2602.08189/) (2026)
- [TERDNet: Transformer Encoder-Recurrent Decoder Network for Scene Change Detection](https://mlsanity.com/paper/2605.20822/) (2026)

## Abstract (arXiv)

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.