Pit30M: A Benchmark for Global Localization in the Age of Self-Driving Cars
Julieta Martinez,Sasha Doubov,Jack Fan,loan Andrei Bârsan,Shenlong Wang,Gellért Máttyus,Raquel Urtasun,Julieta Martinez,Sasha Doubov,Jack Fan,loan Andrei Bârsan,Shenlong Wang,Gellért Máttyus,Raquel Urtasun
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), ...
