GSLAM: A General SLAM Framework and Benchmark
Abstract
SLAM technology has recently seen many successes and attracted the attention of high-technological companies. However, how to unify the interface of existing or emerging algorithms, and effectively perform benchmark about the speed, robustness and portability are still problems. In this paper, we propose a novel SLAM platform named GSLAM, which not only provides evaluation functionality, but also supplies useful toolkit for researchers to quickly develop their SLAM systems. Our core contribution is an universal, cross-platform and full open-source SLAM interface for both research and commercial usage, which is aimed to handle interactions with input dataset, SLAM implementation, visualization and applications in an unified framework. Through this platform, users can implement their own functions for better performance with plugin form and further boost the application to practical usage of the SLAM.
Cite
Text
Zhao et al. "GSLAM: A General SLAM Framework and Benchmark." Proceedings of the IEEE/CVF International Conference on Computer Vision, 2019. doi:10.1109/ICCV.2019.00120Markdown
[Zhao et al. "GSLAM: A General SLAM Framework and Benchmark." Proceedings of the IEEE/CVF International Conference on Computer Vision, 2019.](https://mlanthology.org/iccv/2019/zhao2019iccv-gslam/) doi:10.1109/ICCV.2019.00120BibTeX
@inproceedings{zhao2019iccv-gslam,
title = {{GSLAM: A General SLAM Framework and Benchmark}},
author = {Zhao, Yong and Xu, Shibiao and Bu, Shuhui and Jiang, Hongkai and Han, Pengcheng},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision},
year = {2019},
doi = {10.1109/ICCV.2019.00120},
url = {https://mlanthology.org/iccv/2019/zhao2019iccv-gslam/}
}