# GSLAM: A General SLAM Framework and Benchmark

arXiv: https://arxiv.org/abs/1902.07995 · HTML: https://mlsanity.com/paper/1902.07995/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Feb 2019.

## Built on by (most cited first, 6 of 6)

- [Decoupling Makes Weakly Supervised Local Feature Better](https://mlsanity.com/paper/2201.02861/) (2022)
- [Local Feature Matching Using Deep Learning: A Survey](https://mlsanity.com/paper/2401.17592/) (2024)
- [Real-time 6-DoF Pose Estimation by an Event-based Camera using Active LED Markers](https://mlsanity.com/paper/2310.16618/) (2023)
- [HybridFusion: LiDAR and Vision Cross-Source Point Cloud Fusion](https://mlsanity.com/paper/2304.04508/) (2023)
- [CurriculumLoc: Enhancing Cross-Domain Geolocalization through Multi-Stage Refinement](https://mlsanity.com/paper/2311.11604/) (2023)
- [XRDSLAM: A Flexible and Modular Framework for Deep Learning based SLAM](https://mlsanity.com/paper/2410.23690/) (2024)

## Abstract (arXiv)

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 own SLAM systems. The core contribution of GSLAM 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.