The task of 6D pose estimation with deep learning is to train networks to, from an im-age of an object, determine the rotation and translation of the object. Impressive resultshave recently been shown in deep learning based 6D pose estimation. However, many cur-rent solutions rely on real-world data when training, which as opposed to synthetic data,requires time consuming annotation. In this thesis, we introduce a pipeline for generatingsynthetic ground truth data for deep 6D pose estimation, where annotation is done auto-matically. With a 3D CAD-model, we use Blender to render 2D images of the model fromdifferent view points. We also create all other relevant data needed for pose estimation, e.g.,the poses of an object, mask images and 3D keypoints on the object. Using this pipeline, itis possible to adjust different settings to reduce the domain gap between synthetic data andreal-world data and get better pose estimation results. Such settings could be changing themethod of extracting 3D keypoints and varying the scale of the object or the light settingsin the scene.The network used to test the performance of training on our synthetic data is PVNet,which achieves state-of-the-art results for 6D pose estimation. This architecture learns tofind 2D keypoints of the object in the image, as well as 2D–3D keypoint correspondences.With these correspondences, the Perspective-n-Point (PnP) algorithm is used to extract apose. We evaluate the pose estimation of the different settings on the synthetic data andcompare these results to other state-of-the-art work. We find that using only real-worlddata for training is worse than using a combination of synthetic and real-world data. Sev-eral other findings are that varying scale and lightning, in addition to adding random back-ground images to the rendered images improves results. Four different novel keypoint se-lection methods are introduced in this work, and tried against methods used in previouswork. We observe that our methods achieve similar or better results. Finally, we use thebest possible settings from the synthetic data pipeline, but with memory limitations on theamount of training data. We are close to state-of-the-art results, and could get closer withmore data.

One fundamental task in robotics is random bin-picking, where it is important to be able to detect an object in a bin and estimate its pose to plan the motion of a robotic arm. For this purpose, this thesis work aimed to investigate and evaluate algorithms for 6D pose estimation when the object was given by a CAD model. The scene was given by a point cloud illustrating a partial 3D view of the bin with multiple instances of the object. Two algorithms were thus implemented and evaluated. The first algorithm was an approach based on Point Pair Features, and the second was Fast Global Registration. For evaluation, four different CAD models were used to create synthetic data with ground truth annotations. It was concluded that the Point Pair Feature approach provided a robust localization of objects and can be used for bin-picking. The algorithm appears to be able to handle different types of objects, however, with small limitations when the object has flat surfaces and weak texture or many similar details. The disadvantage with the algorithm was the execution time. Fast Global Registration, on the other hand, did not provide a robust localization of objects and is thus not a good solution for bin-picking.