Candidate processing workflow
SLAM pose → detection/segmentation → depth projection → semantic-map lifecycle
JIVISION offers project development and technical assessment for Mobile-Robot Semantic Mapping and Scene-Object Recognition. Scope: Mapped people, doors, equipment, passages, stations and restricted zones.
Mapped people, doors, equipment, passages, stations and restricted zones
Use different lifecycles for moving objects and fixed assets; people are observed only in authorized operational contexts without sensitive identity inference.
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.
SLAM pose → detection/segmentation → depth projection → semantic-map lifecycle
Provide robot/controller models, grippers/tools, parts or CAD, coordinate frames, working volume, camera mounts, control periods and interlock interfaces.
Semantic map, target coordinates, query interface and update policy
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.
Map localization error; object-map accuracy; duplicate rate; update delay
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.
Mapped people, doors, equipment, passages, stations and restricted zones. Deliverables: Semantic map, target coordinates, query interface and update policy
Use different lifecycles for moving objects and fixed assets; people are observed only in authorized operational contexts without sensitive identity inference.
Task-specific measures: Map localization error; object-map accuracy; duplicate rate; update delay. 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 mapped people, doors, equipment, passages, stations and restricted zones, 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: D11 · Mobile-Robot Semantic Mapping and Scene-Object Recognition
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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