Pit30M: A Benchmark for Global Localization in the Age of Self-Driving Cars

arXiv:2012.12437v2 Announce Type: replace
Abstract: We are interested in understanding whether retrieval-based localization approaches are good enough in the context of self-driving vehicles. Towards this goal, we introduce Pit30M, a new image and LiDAR dataset with over 30 million frames, which is 10 to 100 times larger than those used in previous work. Pit30M is captured under diverse conditions (i.e., season, weather, time of the day, traffic), and provides accurate localization ground truth. We also automatically annotate our dataset with …

age arxiv benchmark cars cs.cv cs.ro driving global localization self-driving type