Introduction - Grasping unknown objects is an important ability for robots in logistic environments. While humans have an excellent understanding of how to grasp objects because of their visual perception and understanding of the 3D world, robotic grasping is still a challenge. Due to the fast-growing development of deep learning methods, it is now possible to train deep neural networks on this grasp task. Objective - This thesis proposes a bin-picking pipeline that uses deep learning to take care of the perception and estimation task. The pipeline can predict grasps for known and unknown objects with a two-fingered pinch-gripper in real-world environments in a single object and multi-object scenes. Method - A grasp annotation tool has been developed to generate a wide variety of grasps in the training data that are antipodal and collision-free. Together with annotated objects, the generated grasps are used to train Grasp-RCNN. The developed Grasp-RCNN combines an object- and a grasp-detection network to predict objects masks and grasps, and a decision algorithm that picks the best-estimated grasp based on a grasp score. Results - Robotic experiments demonstrate that the proposed method allows a robot gripper to grasp both known and unknown objects in single-object and multi-object scenes with a total success-rate of 89.7% and 81.0% with average process-times of 616 ms and 739 ms per scene respectively. In a bin-picking scene a success-rate of 87.5% with a process-time of 1235 ms is achieved. Conclusion - These results indicate that the proposed Grasp-RCNN is able to grasp known and unknown objects with an accuracy that is comparable to the state-of-the-art. For production purposes, the speed of the network still can be improved.