The monocular visual-inertial odometry (VIO) based on the direct method can leverage all available pixels in the image to simultaneously estimate the camera motion and reconstruct the denser map of the scene in real time. However, the direct method is sensitive to photometric changes, which can be compensated by introducing geometric information in the environment. In this paper, we propose a monocular direct sparse visual-inertial odometry, which exploits the planar regularities (PVI-DSO). Our system detects the planar regularities from the 3D mesh built on the estimated map points. To improve the pose estimation accuracy with the geometric information, a tightly coupled coplanar constraint expression is used to express photometric error in the direct method. Additionally, to improve the optimization efficiency, we elaborately derive the analytical Jacobian of the linearization form for the coplanar constraint. Finally, the inertial measurement error, coplanar point photometric error, non-coplanar photometric error, and prior error are added into the optimizer, which simultaneously improves the pose estimation accuracy and mesh itself. We verified the performance of the whole system on simulation and real-world datasets. Extensive experiments have demonstrated that our system outperforms the state-of-the-art counterparts.

翻译：摘要：基于直接法的单目视觉-惯性里程计（VIO）可利用图像中所有可用像素，实时同时估计相机运动并重建场景的稠密地图。然而，直接法对光度变化敏感，而引入环境中的几何信息可对此进行补偿。本文提出一种利用平面正则性的单目直接稀疏视觉-惯性里程计（PVI-DSO）。该系统从基于估计地图点构建的三维网格中检测平面正则性。为利用几何信息提升位姿估计精度，采用紧耦合共面约束表达式来表述直接法中的光度误差。此外，为提升优化效率，我们精心推导了共面约束线性化形式的解析雅可比矩阵。最后，将惯性测量误差、共面点光度误差、非共面光度误差以及先验误差共同纳入优化器，同步提升了位姿估计精度与网格自身质量。我们在仿真与真实数据集上验证了整体系统的性能，大量实验表明，本系统优于当前最先进的同类方案。