Object Perception Pipeline for Industrial Disassembly: From 6D Pose to 3D Localization in Automotive Robotics

Abstract

Industrial disassembly processes require robust and efficient perception systems capable of handling heterogeneous objects under real-world constraints. In automotive manufacturing and disassembly scenarios, components exhibit significant variability in geometry, size, symmetry, and placement, making it impractical to rely on a single, uniform object pose estimation strategy. At the same time, such environments impose strict requirements on the perception systems in terms of reliability, computational efficiency, and ease of deployment.This work presents an instance-based object perception pipeline with task-driven object pose estimation, developed within the SOPRANO EU project, for automated automotive door disassembly. The pipeline assumes a known set of object instances and depending on the objects’ geometric characteristics, the manipulation task requirements , the system performs (i) full 6D pose estimation, (ii) planar-constrained 3D localization via RGB-D lifting, or (iii) planar localization with structured multi-instance refinement and instance identification.The perception process consists of two stages: pose formulation selection and execution of the corresponding estimation method. Model-based 6D pose estimation using RGB data is applied to rigid objects, while RGB-D-based lifting of 2D detections is used for planar or quasi-planar elements. For structured arrangements of multiple instances of a single object type, such as screw arrays, a multi-instance matching strategy ensures consistent indexing and reduces ambiguity.The system is deployed as a modular perception service and validated in an automotive disassembly pilot. Experimental results demonstrate high accuracy across heterogeneous tasks, highlighting the benefits of aligning perception outputs with task-specific requirements.