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• W4312650795 abstract "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 design a complete pipeline for camera pose refinement with points and lines, which contains the innovatively designed line extracting CNN named VLSE, the line matching and the pose optimization approaches. We adopt a novel line representation and customize a hybrid convolution block based on the Stacked Hourglass network [1], to detect accurate and stable line features on images. Then we apply a geometric-based strategy to obtain precise 2D-3D line correspondences using epipolar constraint and reprojection filtering. A following point-line joint cost function is constructed to optimize the camera pose with the initial coarse pose from the pure point-based localization. Sufficient experiments are conducted on open datasets, i.e, line extractor on Wireframe and YorkUrban, localization performance on InLoc ducl and duc2, to confirm the effectiveness of our point-line joint pose optimization method." @default.
• W4312650795 created "2023-01-05" @default.
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• W4312650795 date "2022-10-23" @default.
• W4312650795 modified "2023-10-16" @default.
• W4312650795 title "Pose Refinement with Joint Optimization of Visual Points and Lines" @default.
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• W4312650795 doi "https://doi.org/10.1109/iros47612.2022.9981420" @default.
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