Lines are interesting geometrical features commonly seen in indoor and urban environments. There is missing a complete benchmark where one can evaluate lines from a sequential stream of images in all its stages: Line detection, Line Association and Pose error. To do so, we present a complete and exhaustive benchmark for visual lines in a SLAM front-end, both for RGB and RGBD, by providing a plethora of complementary metrics. We have also labelled data from well-known SLAM datasets in order to have all in one poses and accurately annotated lines. In particular, we have evaluated 17 line detection algorithms, 5 line associations methods and the resultant pose error for aligning a pair of frames with several combinations of detector-association. We have packaged all methods and evaluations metrics and made them publicly available on web-page https://prime-slam.github.io/evolin/.

翻译：线段是室内和城市场景中常见的几何特征。目前尚缺乏一个完整的基准测试，能够从连续图像序列中全面评估线段检测、线段关联及位姿误差各阶段性能。为此，我们构建了一套针对SLAM前端视觉线段（包含RGB和RGBD数据）的完备基准测试体系，提供了丰富的互补评估指标。为确保统一评估，我们对多个知名SLAM数据集中的图像进行了标注，获得了位姿信息与精确标注的线段数据。具体而言，我们评估了17种线段检测算法、5种线段关联方法，以及采用不同检测-关联组合进行帧间配准时的位姿误差。所有方法与评估指标均已封装并公开发布于网页 https://prime-slam.github.io/evolin/。