Visual odometry aims to estimate the relative pose between frames, which is a fundamental task for visual SLAM. In this paper, we present a novel line-based visual odometry (VO) algorithm that fully utilizes the characteristic of line to estimate the projected line of adjacent frame by minimizing the local gradient fitness evaluation. In contrast to the current feature-based or line-based visual odometry, we don${}^{\prime }$t need to explicitly match points or lines of two frames, which is non-trivial and inaccurate in challenging scenarios such as texture-less scenes. In our method, the projected line is calculated simultaneously with the local gradient fitting function of pose estimation based on the constraint that the orientation of the projected line should be perpendicular to the gradient orientation of pixels of its local regions. The proposed method is more robust and reliable than other line-based VO since it fully uses the pixel orientations in the local regions to estimate the projected line and relative pose. We evaluate our method on the real-world RGB-D dataset and synthetic benchmark dataset. Experimental results show that our method achieves the state-of-the-art algorithms in indoors scenes, especially in texture-less scenes.

视觉测距法旨在估计帧之间的相对姿势，这是视觉SLAM的一项基本任务。在本文中，我们提出了一种新颖的基于行的视觉测距（VO）算法，该算法充分利用了行的特征，通过最小化局部梯度适合度评估来估计相邻帧的投影行。与当前基于特征或基于行的视觉里程表相反，我们不${}^{\prime }$需要明确匹配两个帧的点或线，这在具有挑战性的场景（如无纹理的场景）中是不平凡且不准确的。在我们的方法中，基于投影线的方向应垂直于其局部区域像素的梯度方向的约束条件，与姿势估计的局部梯度拟合函数同时计算投影线。所提出的方法比其他基于行的VO更健壮和可靠，因为它充分利用了局部区域中的像素方向来估计投影的行和相对姿势。我们在真实世界的RGB-D数据集和合成基准数据集上评估我们的方法。实验结果表明，该方法在室内场景，尤其是在无纹理场景中达到了最新的算法。