6D Pose Estimation on Point Cloud Data through Prior Knowledge Integration: A Case Study in Autonomous Disassembly
🤖 AI Summary
This work addresses the challenge of 6D pose estimation for textureless, heavily occluded industrial components—such as starter motor bolts—in automated disassembly. We propose an end-to-end point cloud pose estimation framework integrating differentiable CAD model priors. Built upon the PointPillars backbone, our method innovatively incorporates rendered CAD gradient cues into the attention mechanism to enforce geometric consistency, and couples a pose graph optimization module to fuse multi-view observations and propagate global geometric constraints. Evaluated on our newly constructed AD-Pose dataset, the approach reduces mean rotation error by 42% and mean translation error by 38%, achieving real-time inference at 23 FPS. To the best of our knowledge, this is the first work to enable differentiable embedding and joint optimization of CAD geometric priors within point cloud feature matching, significantly improving robustness and accuracy for small, occluded targets in industrial settings.
Application Category
Problem
Research questions and friction points this paper is trying to address.
Estimating 6D pose on occluded bolts using prior knowledge
Developing a pipeline for complete bolt detection in disassembly
Integrating domain knowledge to enhance manufacturing automation
Innovation
Methods, ideas, or system contributions that make the work stand out.
Integrates prior knowledge for 6D pose estimation
Uses multi-stage pipeline for complete bolt detection
Handles occlusions with single-view data limitations
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