# Incremental Mapping with Measurement Synchronization & Compression

arXiv: https://arxiv.org/abs/2602.07901 · HTML: https://mlsanity.com/paper/2602.07901/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 33 references that are in the map.
- First posted on arXiv 8 Feb 2026.

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

- [KISS-ICP: In Defense of Point-to-Point ICP -- Simple, Accurate, and Robust Registration If Done the Right Way](https://mlsanity.com/paper/2209.15397/) (2022)
- [SE-Sync: A Certifiably Correct Algorithm for Synchronization over the Special Euclidean Group](https://mlsanity.com/paper/1612.07386/) (2016)
- [Elasticity Meets Continuous-Time: Map-Centric Dense 3D LiDAR SLAM](https://mlsanity.com/paper/2008.02274/) (2020)
- [An Efficient Global Optimality Certificate for Landmark-Based SLAM](https://mlsanity.com/paper/2206.12961/) (2022)
- [Continuous-Time Fixed-Lag Smoothing for LiDAR-Inertial-Camera SLAM](https://mlsanity.com/paper/2302.07456/) (2023)
- [360ORB-SLAM: A Visual SLAM System for Panoramic Images with Depth Completion Network](https://mlsanity.com/paper/2401.10560/) (2024)
- [Be your own Benchmark: No-Reference Trajectory Metric on Registered Point Clouds](https://mlsanity.com/paper/2106.11351/) (2021)

## Abstract (arXiv)

Modern autonomous vehicles and robots utilize versatile sensors for localization and mapping. The fidelity of these maps is paramount, as an accurate environmental representation is a prerequisite for stable and precise localization. Factor graphs provide a powerful approach for sensor fusion, enabling the estimation of the maximum a posteriori solution. However, the discrete nature of graph-based representations, combined with asynchronous sensor measurements, complicates consistent state estimation. The design of an optimal factor graph topology remains an open challenge, especially in multi-sensor systems with asynchronous data. Conventional approaches rely on a rigid graph structure, which becomes inefficient with sensors of disparate rates. Although preintegration techniques can mitigate this for high-rate sensors, their applicability is limited. To address this problem, this work introduces a novel approach that incrementally constructs connected factor graphs, ensuring the incorporation of all available sensor data by choosing the optimal graph topology based on the external evaluation criteria. The proposed methodology facilitates graph compression, reducing the number of nodes (optimized variables) by ~30% on average while maintaining map quality at a level comparable to conventional approaches.