Pose Refinement with Joint Optimization of Visual Points and Lines

High-precision camera re-localization technology in a pre-established 3D environment map is the basis for many tasks, such as Augmented Reality, Robotics and Autonomous Driving. The point-based visual re-localization approaches are well-developed in recent decades, but are insufficient in some feature-less cases. In this paper, we propose a point-line joint optimization method for pose refinement with the help of the innovatively designed line extracting CNN named VLSE, and the line matching and pose optimization approach. We adopt a novel line representation and customize a hybrid convolutional block based on the Stacked Hourglass network, to detect accurate and stable line features on images. Then we apply a coarse-to-fine strategy to obtain precise 2D-3D line correspondences based on the geometric constraint. A following point-line joint cost function is constructed to optimize the camera pose with the initial coarse pose. Sufficient experiments are conducted on open datasets, i.e, line extractor on Wireframe and YorkUrban, localization performance on Aachen Day-Night v1.1 and InLoc, to confirm the effectiveness of our point-line joint pose optimization method.