For robust visual-inertial SLAM in perceptually-challenging indoor environments,recent studies exploit line features to extract descriptive information about scene structure to deal with the degeneracy of point features. But existing point-line-based SLAM methods mainly use Pl\"ucker matrix or orthogonal representation to represent a line, which needs to calculate at least four variables to determine a line. Given the numerous line features to determine in each frame, the overly flexible line representation increases the computation burden and comprises the accuracy of the results. In this paper, we propose inverse depth representation for a line, which models each extracted line feature using only two variables, i.e., the inverse depths of the two ending points. It exploits the fact that the projected line's pixel coordinates on the image plane are rather accurate, which partially restrict the line. Using this compact line presentation, Inverse Depth Line SLAM (IDLS) is proposed to track the line features in SLAM in an accurate and efficient way. A robust line triangulation method and a novel line re-projection error model are introduced. And a two-step optimization method is proposed to firstly determine the lines and then to estimate the camera poses in each frame. IDLS is extensively evaluated in multiple perceptually-challenging datasets. The results show it is more accurate, robust, and needs lower computational overhead than the current state-of-the-art of point-line-based SLAM methods.

翻译：为在感知挑战性室内环境中实现鲁棒的视觉-惯性SLAM，近期研究利用线特征提取场景结构的描述性信息以应对点特征的退化问题。然而，现有的基于点-线的SLAM方法主要采用普吕克矩阵或正交表示来描述直线，这需要计算至少四个变量以确定一条直线。考虑到每帧图像中需确定的线特征数量众多，这种过度灵活的表征方式增加了计算负担并影响了结果精度。本文提出一种直线的逆深度表示方法，该方法仅使用两个变量（即线段两端点的逆深度）对每条提取的线特征进行建模。该方法利用了投影直线在图像平面上的像素坐标较为准确这一特性，从而对直线形成部分约束。基于这种紧凑的直线表征，我们提出逆深度线SLAM（IDLS）系统，以精确高效的方式在SLAM过程中追踪线特征。本文引入了鲁棒的线特征三角化方法与新颖的直线重投影误差模型，并提出一种两步优化策略：先确定直线参数，再估计每帧图像的相机位姿。IDLS在多个具有感知挑战性的数据集上进行了广泛评估。实验结果表明，相较于当前最先进的基于点-线的SLAM方法，该系统具有更高的精度与鲁棒性，且所需计算开销更低。