EVOLIN Benchmark: Evaluation of Line Detection and Association

• URL: http://arxiv.org/abs/2303.05162v2
• Date: Mon, 31 Jul 2023 11:36:22 GMT
• Title: EVOLIN Benchmark: Evaluation of Line Detection and Association
• Authors: Kirill Ivanov, Gonzalo Ferrer, Anastasiia Kornilova
• Abstract summary: We present a complete benchmark for visual lines in a SLAM front-end, both for RGB and RGBD, by providing a plethora of complementary metrics. We have evaluated 17 line detection algorithms, 5 line associations methods and the resultant pose error for aligning a pair of frames.
• Score: 3.029434408969759
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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/.

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