# ALITA: A Large-scale Incremental Dataset for Long-term Autonomy

arXiv: https://arxiv.org/abs/2205.10737 · HTML: https://mlsanity.com/paper/2205.10737/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 May 2022.

## Built on by (most cited first, 11 of 11)

- [BEVPlace: Learning LiDAR-based Place Recognition using Bird's Eye View Images](https://mlsanity.com/paper/2302.14325/) (2023)
- [Towards Robust Robot 3D Perception in Urban Environments: The UT Campus Object Dataset](https://mlsanity.com/paper/2309.13549/) (2023)
- [General Place Recognition Survey: Towards Real-World Autonomy](https://mlsanity.com/paper/2405.04812/) (2024)
- [Spectral Geometric Verification: Re-Ranking Point Cloud Retrieval for Metric Localization](https://mlsanity.com/paper/2210.04432/) (2022)
- [BioSLAM: A Bio-inspired Lifelong Memory System for General Place Recognition](https://mlsanity.com/paper/2208.14543/) (2022)
- [General Place Recognition Survey: Towards the Real-world Autonomy Age](https://mlsanity.com/paper/2209.04497/) (2022)
- [Register Any Point: Scaling 3D Point Cloud Registration by Flow Matching](https://mlsanity.com/paper/2512.01850/) (2025)
- [SphereVLAD++: Attention-based and Signal-enhanced Viewpoint Invariant Descriptor](https://mlsanity.com/paper/2207.02958/) (2022)
- [GeoAdapt: Self-Supervised Test-Time Adaptation in LiDAR Place Recognition Using Geometric Priors](https://mlsanity.com/paper/2308.04638/) (2023)
- [SALSA: Swift Adaptive Lightweight Self-Attention for Enhanced LiDAR Place Recognition](https://mlsanity.com/paper/2407.08260/) (2024)
- [WHU-PCPR: A cross-platform heterogeneous point cloud dataset for place recognition in complex urban scenes](https://mlsanity.com/paper/2601.06442/) (2026)

## Abstract (arXiv)

For long-term autonomy, most place recognition methods are mainly evaluated on simplified scenarios or simulated datasets, which cannot provide solid evidence to evaluate the readiness for current Simultaneous Localization and Mapping (SLAM). In this paper, we present a long-term place recognition dataset for use in mobile localization under large-scale dynamic environments. This dataset includes a campus-scale track and a city-scale track: 1) the campus-track focuses the long-term property, we record LiDAR device and an omnidirectional camera on 10 trajectories, and each trajectory are repeatly recorded 8 times under variant illumination conditions. 2) the city-track focuses the large-scale property, we mount the LiDAR device on the vehicle and traversing through a 120km trajectories, which contains open streets, residential areas, natural terrains, etc. They includes 200 hours of raw data of all kinds scenarios within urban environments. The ground truth position for both tracks are provided on each trajectory, which is obtained from the Global Position System with an additional General ICP based point cloud refinement. To simplify the evaluation procedure, we also provide the Python-API with a set of place recognition metrics is proposed to quickly load our dataset and evaluate the recognition performance against different methods. This dataset targets at finding methods with high place recognition accuracy and robustness, and providing real robotic system with long-term autonomy. The dataset and the provided tools can be accessed from https://github.com/MetaSLAM/ALITA.