Linebased Absolute and Relative Camera Pose Estimation in Structured Environments 3D lines in structured environments encode particular regularity like parallelism and orthogonality. We leverage this structural regularity to estimate the absolute and relative camera poses. We decouple the rotation and translation, and propose a novel rotation estimation method. We decompose the absolute and relative rotations and reformulate the problem as computing the rotation from the Manhattan frame to the camera frame. To compute this rotation, we propose an accurate and efficient twostep method. We first estimate its two degrees of freedom (DOF) by two image lines, and then estimate its third DOF by another image line. For these lines, we assume their associated 3D lines are mutually orthogonal, or two 3D lines are parallel to each other and orthogonal to the third. Thanks to our twostep DOF estimation, our absolute and relative pose estimation methods are accurate and efficient. Moreover, our relative pose estimation method relies on weaker assumptions or less correspondences than existing approaches. We also propose a novel strategy to reject outliers and identify dominant directions of the scene. We integrate it into our pose estimation methods, and show that it is more robust than RANSAC. Experiments on synthetic and realworld datasets demonstrated that our methods outperform stateoftheart approaches.

Towards the Usage of Synthetic Data for MarkerLess Pose Estimation of Articulated Robots in RGB Images Pose estimation is a necessity for many applications in robotics incorporating interaction between the robot and external cameraequipped devices, e.g. mobile robots or Augmented Reality devices. In the practice of monocular cameras, one mostly takes advantage of pose estimation through fiducial marker detection. We propose a novel approach for markerless robot pose estimation through monocular cameras utilizing 2D keypoint detection and 3D keypoint determination through readings from the encoders and forward kinematics. In particular, 2D3D point correspondences enable the pose estimation through solving the PerspectivenPoint problem for calibrated cameras. The method does not rely on any depth data or initializations. The robust 2D keypoint detection is implemented by modern Convolutional Neural Networks trained on different dataset configurations of real and synthetic data in order to quantitatively evaluate robustness, precision and data efficiency. We demonstrate that the method provides robust pose estimation for random joint poses and benchmark the performance of different (synthetic) dataset configurations. Furthermore, we compare the accuracies to marker pose estimation and give an outlook towards enhancements and realtime capability.