Continuous closerange 3D object pose estimation In the context of future manufacturing lines, removing fixtures will be a fundamental step to increase the flexibility of autonomous systems in assembly and logistic operations. Visionbased 3D pose estimation is a necessity to accurately handle objects that might not be placed at fixed positions during the robot task execution. Industrial tasks bring multiple challenges for the robust pose estimation of objects such as difficult object properties, tight cycle times and constraints on camera views. In particular, when interacting with objects, we have to work with closerange partial views of objects that pose a new challenge for typical viewbased pose estimation methods.In this paper, we present a 3D pose estimation method based on a gradientascend particle filter that integrates new observations onthefly to improve the pose estimate. Thereby, we can apply this method online during task execution to save valuable cycle time. In contrast to other viewbased pose estimation methods, we model potential views in full 6dimensional space that allows us to cope with closerange partial objects views. We demonstrate the approach on a real assembly task, in which the algorithm usually converges to the correct pose within 1015 iterations with an average accuracy of less than 8mm.

Combined Visual and Touchbased Sensing for the Autonomous Registration of Objects with Circular Features Future manufacturing systems will have to allow frequent conversion of production processes. In order to prevent a surge in setup times and the cost involved, we suggest refraining from specialized part feeders, fixture units and manual calibration routines. To this effect we regard autonomous object registration as corner stone to lower the manual calibration effort. In this work, we propose a framework for autonomous object registration in robotic workcells using a combination of visual and touchbased sensing. Vision systems in process automation often require very defined conditions to produce reliable pose estimates. Therefore, we combine data from a vision system with touchbased sensing in order to benefit from both sensing modalities and reduce the requirements on the vision system. We use a particle filter approach in order to estimate the pose of individual features and show how to retrieve the overall pose of an object in the workcell from the feature estimates. The observed feature distribution is used to autonomously trigger dedicated actions for touchbased probing with a lightweight robotic arm in order to increase the accuracy. In particular, we describe the detection of circular features and validated the framework in experiments with our robotic assembly system.