Experience-based optimization of robotic perception

As the performance of key perception tasks heavily depends on their parametrization, deploying versatile robots to different application domains will also require a way to tune these changing scenarios by their operators. As many of these tunings are found by trial and error basically by experts as well, and the quality criteria change from application to application, we propose a Pipeline Optimization Framework that helps overcoming lengthy setup times by largely automating this process. When deployed, fine-tuning optimizations as presented in this paper can be initiated on pre-recorded data, dry runs, or automatically during operation. Here, we quantified the performance gains for two crucial modules based on ground truth annotated data. We release our challenging THR dataset, including evaluation scenes for two application scenarios.