  6D object pose estimation holds essential roles in various fields,
particularly in the grasping of industrial workpieces. Given challenges like
rust, high reflectivity, and absent textures, this paper introduces a point
cloud based pose estimation framework (PS6D). PS6D centers on slender and
multi-symmetric objects. It extracts multi-scale features through an
attention-guided feature extraction module, designs a symmetry-aware rotation
loss and a center distance sensitive translation loss to regress the pose of
each point to the centroid of the instance, and then uses a two-stage
clustering method to complete instance segmentation and pose estimation.
Objects from the Sil\'eane and IPA datasets and typical workpieces from
industrial practice are used to generate data and evaluate the algorithm. In
comparison to the state-of-the-art approach, PS6D demonstrates an 11.5\%
improvement in F$_{1_{inst}}$ and a 14.8\% improvement in Recall. The main part
of PS6D has been deployed to the software of Mech-Mind, and achieves a 91.7\%
success rate in bin-picking experiments, marking its application in industrial
pose estimation tasks.
