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.

PoseConvGRU: A Monocular Approach for Visual Egomotion Estimation by Learning Abstract   Visual egomotion estimation is one of the longstanding problems which estimates the movement of cameras from images. Learning based egomotion estimation methods have seen an increasing attention since its desirable properties of robustness to image noise and camera calibration independence. In this work, we propose a datadriven approach of learning based visual egomotion estimation for a monocular camera. We use an endtoend learning approach in allowing the model to learn a map from input image pairs to the corresponding egomotion, which is parameterized as 6DoF transformation matrix. We introduce a twomodule Longterm Recurrent Convolutional Neural Networks called PoseConvGRU. The featureencoding module encodes the shortterm motion feature in an image pair, while the memorypropagating module captures the longterm motion feature in the consecutive image pairs. The visual memory is implemented with convolutional gated recurrent units, which allows propagating information over time. At each time step, two consecutive RGB images are stacked together to form a 6channel tensor for featureencoding module to learn how to extract motion information and estimate poses. The sequence of output maps is then passed through the memorypropagating module to generate the relative transformation pose of each image pair. In addition, we have designed a series of data augmentation methods to avoid the overfitting problem and improve the performance of the model when facing challengeable scenarios such as highspeed or reverse driving. We evaluate the performance of our proposed approach on the KITTI Visual Odometry benchmark and Malaga 2013 Dataset. The experiments show a competitive performance of the proposed method to the stateoftheart monocular geometric and learning methods and encourage further exploration of learningbased methods for the purpose of estimating camera egomotion even though geometrical methods demonstrate promising results.