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

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