# LT-mapper: A Modular Framework for LiDAR-based Lifelong Mapping

arXiv: https://arxiv.org/abs/2107.07712 · HTML: https://mlsanity.com/paper/2107.07712/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jul 2021.

## Built on by (most cited first, 9 of 9)

- [General Place Recognition Survey: Towards the Real-world Autonomy Age](https://mlsanity.com/paper/2209.04497/) (2022)
- [VMA: Divide-and-Conquer Vectorized Map Annotation System for Large-Scale Driving Scene](https://mlsanity.com/paper/2304.09807/) (2023)
- [LT-Gaussian: Long-Term Map Update Using 3D Gaussian Splatting for Autonomous Driving](https://mlsanity.com/paper/2508.01704/) (2025)
- [Gaussian Mapping for Evolving Scenes](https://mlsanity.com/paper/2506.06909/) (2025)
- [LT-Mem: Volatility-Aware Spatio-Temporal Memory for Lifelong Scene Understanding](https://mlsanity.com/paper/2608.19059/) (2026)
- [Lifelong 3D Mapping Framework for Hand-held & Robot-mounted LiDAR Mapping Systems](https://mlsanity.com/paper/2501.18110/) (2025)
- [The City that Never Settles: Simulation-based LiDAR Dataset for Long-Term Place Recognition Under Extreme Structural Changes](https://mlsanity.com/paper/2505.05076/) (2025)
- [Chamelion: Reliable Change Detection for Long-Term LiDAR Mapping in Transient Environments](https://mlsanity.com/paper/2602.08189/) (2026)
- [A Review Of Robotic World Models For Dynamic Environments Based On Factor And Scene Graphs](https://mlsanity.com/paper/2610.08800/) (2026)

## Abstract (arXiv)

Long-term 3D map management is a fundamental capability required by a robot to reliably navigate in the non-stationary real-world. This paper develops open-source, modular, and readily available LiDAR-based lifelong mapping for urban sites. This is achieved by dividing the problem into successive subproblems: multi-session SLAM (MSS), high/low dynamic change detection, and positive/negative change management. The proposed method leverages MSS and handles potential trajectory error; thus, good initial alignment is not required for change detection. Our change management scheme preserves efficacy in both memory and computation costs, providing automatic object segregation from a large-scale point cloud map. We verify the framework's reliability and applicability even under permanent year-level variation, through extensive real-world experiments with multiple temporal gaps (from day to year).