  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. Vision-based 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 close-range partial views of objects that
pose a new challenge for typical view-based pose estimation methods. In this
paper, we present a 3D pose estimation method based on a gradient-ascend
particle filter that integrates new observations on-the-fly to improve the pose
estimate. Thereby, we can apply this method online during task execution to
save valuable cycle time. In contrast to other view-based pose estimation
methods, we model potential views in full 6- dimensional space that allows us
to cope with close-range partial objects views. We demonstrate the approach on
a real assembly task, in which the algorithm usually converges to the correct
pose within 10-15 iterations with an average accuracy of less than 8mm.
