GSLAM: A General SLAM Framework and Benchmark

TL;DR

GSLAM is a comprehensive, open-source, cross-platform SLAM framework that unifies algorithm interfaces, facilitates benchmarking, and accelerates development and deployment of SLAM systems.

Contribution

It introduces a universal, open-source SLAM platform that unifies interfaces, supports benchmarking, and enables rapid development for research and commercial applications.

Findings

Provides a unified interface for SLAM algorithms

Enables benchmarking of speed, robustness, and portability

Supports quick development with plugin-based architecture

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 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.