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}.

翻译：本文介绍了一个名为Open-Structure的新型基准数据集，用于评估视觉里程计和SLAM方法。该数据集直接提供点线测量值、对应关系、结构关联及共视因子图，而非原始图像。基于该基准数据集，这些2D或3D数据可直接输入SLAM流水线的不同阶段，从而在消融实验中避免数据预处理模块的影响。首先，我们提出了一种适用于真实场景与仿真场景的数据集生成器。在真实场景中，该生成器保持与实际特征提取结果相同的观察与遮挡关系；而生成的仿真序列通过引入各种精心设计的轨迹与观察方式，增强了数据集的多样性。其次，我们基于该数据集提出了SLAM基线，用于评估相机位姿追踪、参数化及优化模块中广泛使用的模块。通过在不同场景下评估这些最先进算法，我们明确了各模块在相机追踪与优化过程中的优势与不足。本数据集及基线可于\url{https://github.com/yanyan-li/Open-Structure}获取。