  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/.
