Space debris is an ever growing concern for humans ambitions in space. Thousands of objects are currentlytracked to make sure no collisions with expensive hardware or even crewed spacecraft happen. Stricterlaws regarding the end-of-life of space hardware are expected to be introduced in the coming years. However, even with strictest laws, there is already a large amount of objects around earth, that will take thousands of years to reenter the earth atmosphere.This work is part of a project on active space debris removal from orbit using a dedicated space probe. This space probe is doing the final approach to a target object using a camera based navigation system. For training and evaluation of such a system, a labeled data set is needed. This work introduces a Blender based rendering pipeline for generating large training datasets for the application of space debris detection using camera data. The approach described in this work allows parameterized data set generation for a multitude of possible environmental conditions. The flexible nature of this approach allows the tool to be used also for other machine learning applications where object detection, localization and image segmentation might be of interest.Preliminary tests were made to evaluate the possibilities of such data sets. Different approaches were compared and evaluated on the basis of the constraint environment of a space probe, where size and power constraints as well as the radiation environment represent major challenges in regards to computing resources available to the localization algorithms. The evaluations suggests, that with the help of this rendering pipeline a ML approach is indeed feasible.
