  This paper introduces a new benchmark dataset, Open-Structure, for evaluating
visual odometry and SLAM methods, which directly equips point and line
measurements, correspondences, structural associations, and co-visibility
factor graphs instead of providing raw images. Based on the proposed benchmark
dataset, these 2D or 3D data can be directly input to different stages of SLAM
pipelines to avoid the impact of the data preprocessing modules in ablation
experiments. First, we propose a dataset generator for real-world and simulated
scenarios. In real-world scenes, it maintains the same observations and
occlusions as actual feature extraction results. Those generated simulation
sequences enhance the dataset's diversity by introducing various carefully
designed trajectories and observations. Second, a SLAM baseline is proposed
using our dataset to evaluate widely used modules in camera pose tracking,
parametrization, and optimization modules. By evaluating these state-of-the-art
algorithms across different scenarios, we discern each module's strengths and
weaknesses within the camera tracking and optimization process. Our dataset and
baseline are available at \url{https://github.com/yanyan-li/Open-Structure}.
