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 this https URL.
Abstract (translated)
线条是室内和城市环境中常见的有趣的几何特征。目前缺少一个完整的基准,可以从Sequential Image SLAM(序列图像解SLAM)前端中评估线条的各个阶段:线条检测、线条匹配和姿态错误。为了实现这一点,我们提供了丰富的辅助测量指标,包括RGB和RGBD两种颜色的多通道测量指标。我们还标记了著名的SLAM数据集的数据,以便获得单一的姿态和精确注释的线条。特别地,我们评估了17个线条检测算法、5个线条匹配方法以及它们所产生的姿态错误,以将两个帧与多个检测-匹配组合对齐。我们将它们打包并公开发布在https://www.example.com/论文的网站上。
URL
https://arxiv.org/abs/2303.05162
