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PL-VINS: Real-Time Monocular Visual-Inertial SLAM with Point and Line Features
arXiv - CS - Robotics Pub Date : 2020-09-16 , DOI: arxiv-2009.07462
Qiang Fu, Jialong Wang, Hongshan Yu, Islam Ali, Feng Guo, Yijia He, Hong Zhang

Leveraging line features to improve localization accuracy of point-based visual-inertial SLAM (VINS) is gaining interest as they provide additional constraints on scene structure. However, real-time performance when incorporating line features in VINS has not been addressed. This paper presents PL-VINS, a real-time optimization-based monocular VINS method with point and line features, developed based on the state-of-the-art point-based VINS-Mono \cite{vins}. We observe that current works use the LSD \cite{lsd} algorithm to extract line features; however, LSD is designed for scene shape representation instead of the pose estimation problem, which becomes the bottleneck for the real-time performance due to its high computational cost. In this paper, a modified LSD algorithm is presented by studying a hidden parameter tuning and length rejection strategy. The modified LSD can run at least three times as fast as LSD. Further, by representing space lines with the Pl\"{u}cker coordinates, the residual error in line estimation is modeled in terms of the point-to-line distance, which is then minimized by iteratively updating the minimum four-parameter orthonormal representation of the Pl\"{u}cker coordinates. Experiments in a public benchmark dataset show that the localization error of our method is 12-16\% less than that of VINS-Mono at the same pose update frequency. %For the benefit of the community, The source code of our method is available at: https://github.com/cnqiangfu/PL-VINS.

中文翻译:

PL-VINS:具有点和线特征的实时单目视觉惯性 SLAM

利用线特征来提高基于点的视觉惯性 SLAM (VINS) 的定位精度越来越受到关注,因为它们对场景结构提供了额外的约束。然而,在 VINS 中结合线特征时的实时性能尚未得到解决。本文介绍了 PL-VINS,这是一种基于实时优化的具有点和线特征的单目 VINS 方法,它基于最先进的基于点的 VINS-Mono \cite{vins} 开发。我们观察到当前的作品使用 LSD \cite{lsd} 算法来提取线特征;然而,LSD 是为场景形状表示而不是姿态估计问题而设计的,由于其高计算成本,这成为实时性能的瓶颈。在本文中,通过研究隐藏参数调整和长度拒绝策略,提出了一种改进的 LSD 算法。修改后的 LSD 的运行速度至少是 LSD 的三倍。此外,通过用 Pl\"{u}cker 坐标表示空间线,线估计中的残差根据点到线距离建模,然后通过迭代更新最小四参数正交表示来最小化Pl\"{u}cker 坐标。在公共基准数据集中的实验表明,在相同的姿势更新频率下,我们的方法的定位误差比 VINS-Mono 的定位误差小 12-16\%。%为了社区的利益,我们方法的源代码位于:https://github.com/cnqiangfu/PL-VINS。通过用 Pl\"{u}cker 坐标表示空间线,线估计中的残差根据点到线距离建模,然后通过迭代更新最小四参数正交表示来最小化Pl\"{u}cker 坐标。在公共基准数据集中的实验表明,在相同的姿势更新频率下,我们的方法的定位误差比 VINS-Mono 的定位误差小 12-16%。%为了社区的利益,我们方法的源代码位于:https://github.com/cnqiangfu/PL-VINS。通过用 Pl\"{u}cker 坐标表示空间线,线估计中的残差根据点到线距离建模,然后通过迭代更新最小四参数正交表示来最小化Pl\"{u}cker 坐标。在公共基准数据集中的实验表明,在相同的姿势更新频率下,我们的方法的定位误差比 VINS-Mono 的定位误差小 12-16%。%为了社区的利益,我们方法的源代码位于:https://github.com/cnqiangfu/PL-VINS。在公共基准数据集中的实验表明,在相同的姿势更新频率下,我们的方法的定位误差比 VINS-Mono 的定位误差小 12-16%。%为了社区的利益,我们方法的源代码位于:https://github.com/cnqiangfu/PL-VINS。在公共基准数据集中的实验表明,在相同的姿势更新频率下,我们的方法的定位误差比 VINS-Mono 的定位误差小 12-16%。%为了社区的利益,我们方法的源代码位于:https://github.com/cnqiangfu/PL-VINS。
更新日期:2020-10-13
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