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/.
@article{arxiv.2303.05162,
title = {EVOLIN Benchmark: Evaluation of Line Detection and Association},
author = {Kirill Ivanov and Gonzalo Ferrer and Anastasiia Kornilova},
journal= {arXiv preprint arXiv:2303.05162},
year = {2023}
}