Characterizing Visual Localization and Mapping Datasets

Sajad Saeedi, Eduardo Carvalho, Wenbin Li, Dimos Tzoumanikas, Stefan Leutenegger, Paul Kelly, Andrew Davison

Research output: Chapter or section in a book/report/conference proceedingChapter in a published conference proceeding

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Benchmarking mapping and motion estimation algorithms is established practice in robotics and computer vision. As the diversity of datasets increases, in terms of the trajectories, models, and scenes, it becomes a challenge to select datasets for a given benchmarking purpose. Inspired by the Wasserstein distance, this paper addresses this concern by developing novel metrics to evaluate trajectories and the environments without relying on any SLAM or motion estimation algorithm. The metrics, which so far have been missing in the research community, can be applied to the plethora of datasets that exist. Additionally, to improve the robotics SLAM benchmarking, the paper presents a new dataset for visual localization and mapping algorithms. A broad range of real-world trajectories is used in very high-quality scenes and a rendering framework to create a set of synthetic datasets with groundtruth trajectory and dense map which are representative of key SLAM applications such as virtual reality (VR), micro aerial vehicle (MAV) flight, and ground robotics.
Original languageEnglish
Title of host publicationIEEE/RSJ International Conference on Robotics and Automation (ICRA)​
Publication statusAcceptance date - 1 Jun 2019

Publication series

NameProceedings - International Conference on Robotics and Automation
ISSN (Print)1050-4729


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