# Lifelong 3D Mapping Framework for Hand-held & Robot-mounted LiDAR Mapping Systems

arXiv: https://arxiv.org/abs/2501.18110 · HTML: https://mlsanity.com/paper/2501.18110/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 30 references that are in the map.
- First posted on arXiv 30 Jan 2025.

## Builds on (most cited first, 7 of 7)

- [ERASOR: Egocentric Ratio of Pseudo Occupancy-based Dynamic Object Removal for Static 3D Point Cloud Map Building](https://mlsanity.com/paper/2103.04316/) (2021)
- [BioSLAM: A Bio-inspired Lifelong Memory System for General Place Recognition](https://mlsanity.com/paper/2208.14543/) (2022)
- [LT-mapper: A Modular Framework for LiDAR-based Lifelong Mapping](https://mlsanity.com/paper/2107.07712/) (2021)
- [Iterative Global Similarity Points : A robust coarse-to-fine integration solution for pairwise 3D point cloud registration](https://mlsanity.com/paper/1808.03899/) (2018)
- [Geometry-based Graph Pruning for Lifelong SLAM](https://mlsanity.com/paper/2110.01286/) (2021)
- [A General Framework for Lifelong Localization and Mapping in Changing Environment](https://mlsanity.com/paper/2111.10946/) (2021)
- [Lifelong update of semantic maps in dynamic environments](https://mlsanity.com/paper/2010.08846/) (2020)

## Abstract (arXiv)

We propose a lifelong 3D mapping framework that is modular, cloud-native by design and more importantly, works for both hand-held and robot-mounted 3D LiDAR mapping systems. Our proposed framework comprises of dynamic point removal, multi-session map alignment, map change detection and map version control. First, our sensor-setup agnostic dynamic point removal algorithm works seamlessly with both hand-held and robot-mounted setups to produce clean static 3D maps. Second, the multi-session map alignment aligns these clean static maps automatically, without manual parameter fine-tuning, into a single reference frame, using a two stage approach based on feature descriptor matching and fine registration. Third, our novel map change detection identifies positive and negative changes between two aligned maps. Finally, the map version control maintains a single base map that represents the current state of the environment, and stores the detected positive and negative changes, and boundary information. Our unique map version control system can reconstruct any of the previous clean session maps and allows users to query changes between any two random mapping sessions, all without storing any input raw session maps, making it very unique. Extensive experiments are performed using hand-held commercial LiDAR mapping devices and open-source robot-mounted LiDAR SLAM algorithms to evaluate each module and the whole 3D lifelong mapping framework.