# Long-term map maintenance pipeline for autonomous vehicles

arXiv: https://arxiv.org/abs/2008.12449 · HTML: https://mlsanity.com/paper/2008.12449/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Aug 2020.

## Built on by (most cited first, 5 of 5)

- [Pix2Map: Cross-modal Retrieval for Inferring Street Maps from Images](https://mlsanity.com/paper/2301.04224/) (2023)
- [MapRF: Weakly Supervised Online HD Map Construction via NeRF-Guided Self-Training](https://mlsanity.com/paper/2511.19527/) (2025)
- [InterLoc: LiDAR-based Intersection Localization using Road Segmentation with Automated Evaluation Method](https://mlsanity.com/paper/2505.00512/) (2025)
- [Been There, Scanned That: Nostalgia-Driven LiDAR Compression for Self-Driving Cars](https://mlsanity.com/paper/2511.00652/) (2025)
- [GaussianMap: Learning Gaussian Representation for Multi-Sensor Online HD Map Construction](https://mlsanity.com/paper/2606.31177/) (2026)

## Abstract (arXiv)

For autonomous vehicles to operate persistently in a typical urban environment, it is essential to have high accuracy position information. This requires a mapping and localisation system that can adapt to changes over time. A localisation approach based on a single-survey map will not be suitable for long-term operation as it does not incorporate variations in the environment. In this paper, we present new algorithms to maintain a featured-based map. A map maintenance pipeline is proposed that can continuously update a map with the most relevant features taking advantage of the changes in the surroundings. Our pipeline detects and removes transient features based on their geometrical relationships with the vehicle's pose. Newly identified features became part of a new feature map and are assessed by the pipeline as candidates for the localisation map. By purging out-of-date features and adding newly detected features, we continually update the prior map to more accurately represent the most recent environment. We have validated our approach using the USyd Campus Dataset, which includes more than 18 months of data. The results presented demonstrate that our maintenance pipeline produces a resilient map which can provide sustained localisation performance over time.