Characterizing visual localization and mapping datasets

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Title: Characterizing visual localization and mapping datasets
Authors: Saeedi, S
Carvalho, EDC
Li, W
Tzoumanikas, D
Leutenegger, S
Kelly, PHJ
Davison, AJ
Item Type: Conference Paper
Abstract: 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 ground-truth trajectory and dense map which are representative of key SLAM applications such as virtual reality (VR), micro aerial vehicle (MAV) flight, and ground robotics.
Issue Date: 12-Aug-2019
Date of Acceptance: 20-May-2019
ISBN: 9781538681763
ISSN: 1050-4729
Publisher: Institute of Electrical and Electronics Engineers
Journal / Book Title: 2019 International Conference on Robotics and Automation (ICRA)
Copyright Statement: © 2019 IEEE.
Sponsor/Funder: Engineering & Physical Science Research Council (E
Funder's Grant Number: PO: ERZ1820653
Conference Name: 2019 International Conference on Robotics and Automation (ICRA)
Publication Status: Published
Start Date: 2019-05-20
Finish Date: 2019-05-24
Conference Place: Montreal, QC, Canada
Online Publication Date: 2019-08-12
Appears in Collections:Computing

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