Line-based Camera Pose Estimation in Point Cloud of Structured Environments
This addresses camera localization and calibration challenges in robotics and computer vision, but it is an incremental improvement focusing on specific geometric constraints in structured environments.
The paper tackles the problem of automatically registering 2D imagery with untextured 3D point clouds in structured environments by proposing a method that estimates camera pose using 2D-3D line feature correspondences, achieving effective results on synthetic and real datasets with repeated structures and rapid depth changes.
Accurate registration of 2D imagery with point clouds is a key technology for image-LiDAR point cloud fusion, camera to laser scanner calibration and camera localization. Despite continuous improvements, automatic registration of 2D and 3D data without using additional textured information still faces great challenges. In this paper, we propose a new 2D-3D registration method to estimate 2D-3D line feature correspondences and the camera pose in untextured point clouds of structured environments. Specifically, we first use geometric constraints between vanishing points and 3D parallel lines to compute all feasible camera rotations. Then, we utilize a hypothesis testing strategy to estimate the 2D-3D line correspondences and the translation vector. By checking the consistency with computed correspondences, the best rotation matrix can be found. Finally, the camera pose is further refined using non-linear optimization with all the 2D-3D line correspondences. The experimental results demonstrate the effectiveness of the proposed method on the synthetic and real dataset (outdoors and indoors) with repeated structures and rapid depth changes.