The interaction of robots with their environment requires robust object-centric perception capabilities, typically achieved using learning-based methods trained on synthetic data. However, real-world deployment demands evaluating these capabilities in relevant environments, often involving extensive manual annotation for a quantitative analysis. Additionally, standardized evaluations for robotic tasks, such as grasping, need reproducible object scene configurations and performance benchmarks. We propose a solution to both problems by temporarily employing 3D-printed components, socalled fixtures, which can be designed for any rigid object. Once the scene is set up and object poses are extracted, the fixtures are removed, leaving the natural scene without any artificial distractions. The presented approach is seemingly applicable for pre-determined configurations of multiple objects, which enables precise re-building of scenes with consistent object-toobject relations. Our suggested annotation procedure achieves strong pose accuracy solely on RGB images without any manual involvement. We evaluate and show the usability of the proposed fixtures for automated real-world data annotation to fine-tune a detector and for benchmarking object pose estimation algorithms for robotic grasping. Code and fixture meshes for 3D printing are available at https://github.com/DLRRM/fixture_generation.

Heterogeneous teams of multiple mobile robots will be important for future scientific explorations of extraterrestrial surfaces or hazardous areas. Mission operation in such harsh, unknown environments poses diverse challenges. Robots need to cooperate autonomously due to the large network latency to the ground station while operators need to adapt the ongoing mission flexibly based on new discoveries obtained during execution. Furthermore, shared situational awareness between operators and roboticists is highly required to deal with execution failures promptly. To overcome these challenges, this paper proposes the high-level mission operation framework ROSMC. The concept of mission synchronization to robots enables continuous mission adaptations and future planning by operators while robots execute the mission autonomously. The ROS-based GUIs enable operators to intuitively create and monitor the mission for robots as well as to communicate with roboticists smoothly. The proposed framework was evaluated by a pilot study with a simulator and demonstrated at a Moon-analogue field on Mt. Etna in Sicily, Italy, involving 3 robots and around 70 researchers for 4 weeks.