Candidate processing workflow
RGB-D/point-cloud segmentation → FPFH/RANSAC → ICP; textured objects may use feature matching and PnP
JIVISION offers project development and technical assessment for 3D Random Bin Picking and 6D Pose Estimation. Scope: Random bin picking, occlusion, piled objects and spatial poses.
Random bin picking, occlusion, piled objects and spatial poses
Score pose estimation separately from grasp success; validate gripper, center of mass, collisions and reachability with the robot system.
These are candidate implementation paths. Establish a baseline on real samples, then select or combine methods for imaging, speed and deployment constraints. Model names do not imply measured project results.
RGB-D/point-cloud segmentation → FPFH/RANSAC → ICP; textured objects may use feature matching and PnP
Provide robot/controller models, grippers/tools, parts or CAD, coordinate frames, working volume, camera mounts, control periods and interlock interfaces.
Pose module, grasp points, point-cloud interface and failure codes
Specify input formats, output fields, coordinates/units, error states, versions and invocation methods. Define review and failure handling. The statement of work determines the exact scope of source code, executables, model files or analysis reports.
ADD/ADD-S; pose error; grasp success; per-pick cycle
Test visual localization error, calibration error, grasp/alignment success and end-to-end cycle time separately, including occlusion, empty scenes, changeovers and communication loss.
These are measures to agree and test, not achieved-performance claims. Freeze samples, reference truth, thresholds and hardware/software versions before acceptance; report subgroup results and failures, identifying under-sampled conditions as uncovered.
Agree targets, stations, inputs and responsibilities in a statement of work, separating required and excluded conditions.
Use representative samples to test critical risks, document feasibility/failures and scope the next-stage estimate.
Implement agreed functions and interfaces with configuration/change records; use offline replay before authorized device or site integration.
Retest against individual measures and hand over contracted artifacts with known limits; manage maintenance, expansion and changeovers as subsequent work packages.
Vision output does not replace independent safety control. Responsible teams must verify robot motion, tooling and line interlocks before integrated testing.
Candidate technologies are not license clearance. Check code, model weights, training data and dependency versions separately. Replace, license or exclude components unsuitable for the intended commercial delivery. Customer data is not used for public training by default.
Random bin picking, occlusion, piled objects and spatial poses. Deliverables: Pose module, grasp points, point-cloud interface and failure codes
Score pose estimation separately from grasp success; validate gripper, center of mass, collisions and reachability with the robot system.
Task-specific measures: ADD/ADD-S; pose error; grasp success; per-pick cycle. Test visual localization error, calibration error, grasp/alignment success and end-to-end cycle time separately, including occlusion, empty scenes, changeovers and communication loss.
After reviewing samples for random bin picking, occlusion, piled objects and spatial poses, equipment conditions and interfaces, scope validation, development, deployment and acceptance separately. Data coverage, site changes and delivery rights affect the estimate; no fixed performance or schedule is promised before assessment.
Service ID: D03 · 3D Random Bin Picking and 6D Pose Estimation
Describe available samples, equipment and target measures. Agree confidentiality and permissions before transferring sensitive or personal data through an approved channel.
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