In this section
Below is a list of all relevant publications authored by Robotics Forum members.
@inproceedings{Saeedi:2019:10.1109/icra.2019.8793528,
author = {Saeedi, S and Carvalho, EDC and Li, W and Tzoumanikas, D and Leutenegger, S and Kelly, PHJ and Davison, AJ},
doi = {10.1109/icra.2019.8793528},
publisher = {Institute of Electrical and Electronics Engineers},
title = {Characterizing visual localization and mapping datasets},
url = {http://dx.doi.org/10.1109/icra.2019.8793528},
year = {2019}
}
TY - CPAPER
AB - 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.
AU - Saeedi,S
AU - Carvalho,EDC
AU - Li,W
AU - Tzoumanikas,D
AU - Leutenegger,S
AU - Kelly,PHJ
AU - Davison,AJ
DO - 10.1109/icra.2019.8793528
PB - Institute of Electrical and Electronics Engineers
PY - 2019///
SN - 1050-4729
TI - Characterizing visual localization and mapping datasets
UR - http://dx.doi.org/10.1109/icra.2019.8793528
UR - http://hdl.handle.net/10044/1/73403
ER -
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