EDPLVO:Efficient Direct Point-Line Visual Odometry note

ABSTRACT

This paper extends the photometric error for lines.  we proved that the 3D points of the points on a 3D line are determined by the inverse depths of the endpoints of the 2D line, and derive a closed-form solution for this problem.   This property can significantly reduce the number of variables to speed up the optimazition, and can make the collinear constraint exactly satisfied. Furthermore, we introduce a two-step method to further accelerate the optimization, and prove the convergence of this method.The experimental results show that our algorithm outperforms the state-of-the-art direct VO algorithms.

能否在不增加计算负荷的情况下利用共线性

为此他们在之前工作DPLVO的基础上进行了进一步的研究。并提出了一种新的算法——EDPLVO。

特征法和直接法参考EDPLVO: Efficient Direct Point-Line Visual Odometry论文笔记_努力glow .的博客-CSDN博客_line matching slam

作者提出了一种使用点和线的高效的直接视觉里程计visual odometryVO算法—— EDPLVO .他们证明了2D 线上的 3D 像素点由 2D 线端点的逆深度决定这使得将光度误差扩展到线变得可行。

论文中使用的线段提取方法是LSD而LSD本身存在的问题是受到运动模糊或者取整的影响线段提取本身存在一定的不确定性不确定性反应在线段的宽度上。这里个人理解是线段可能并不准确会由于抖动、模糊等原因让坐标不准确也就是说提取出来的一条线段其周围一定范围可能仍然属于这条线段。基于此论文对线段增加了一个支持域支持域宽度用ρ表示以此来衡量线段的不确定性ρ越大说明线段提取结果周围很大的范围都可能是线段的一部分所以不确定性大。

所谓共线约束实际上就是指一条线段在所有观测到的图像上检测的2d线段反过来投影其端点应该保持共线.

Experiments

The ICL-NUIM dataset contains 8 indoor sequences, and the TUM mono VO dataset has 50 loop-closed sequences from indoor and outdoor environments. 

Related-Work:

Although deep learning based line detection methods [19]–[23] show promising results, these methods are generally computationally demanding.

"deep hough transform for semantic line detection,” IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021

"Holistically-attracted wireframe parsing," in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition,2020,

"learning point-pair graph for line segment detection," in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019,