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Record W2091094332 · doi:10.1177/0278364911425836

An autonomous six-DOF eye-in-hand system for in situ 3D object modeling

2011· article· en· W2091094332 on OpenAlexaff
Liila Torabi, Kamal Gupta

Bibliographic record

VenueThe International Journal of Robotics Research · 2011
Typearticle
Languageen
FieldEngineering
TopicRobotics and Sensor-Based Localization
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsObject (grammar)Computer visionComputer sciencePath (computing)Artificial intelligenceWorkspaceA priori and a posterioriReachabilityMotion planningRobotic armBounding overwatchObserver (physics)Object modelRobotAlgorithm

Abstract

fetched live from OpenAlex

We present an integrated and fully autonomous eye-in-hand system for 3D object modeling. The system hardware consists of a laser range scanner mounted on a six-DOF manipulator arm and the task is to autonomously build a 3D model of an object in situ where the object may not be moved and must be scanned in its original location. Our system assumes no knowledge of object shape or geometry other than that it is within a bounding box whose location and size are known a priori, and, furthermore, the environment is unknown. The overall planner integrates the three main algorithms in the system: one that finds the next best view (NBV) for modeling the object; one that finds the NBV for exploration, i.e. exploring the environment, so the arm can move to the modeling view pose; and finally a sensor-based path planner, that is able to find a collision-free path to the view configuration determined by either of the the two view planners. Our modeling NBV algorithm efficiently searches the five-dimensional view space to determine the best modeling viewpoint, while considering key constraints such as field of view (FOV), overlap, and occlusion. If the determined viewpoint is reachable, the sensor-based path planner determines a collision-free path to move the manipulator to the desired view configuration, and a scan of the object is taken. Since the workspace is initially unknown, in some phases, the exploration view planner is used to increase information about the reachability and also the status of the modeling view configurations, since the view configuration may lie in an unknown workspace. This is repeated until the object modeling is complete or the planner deems that no further progress can be made, and the system stops. We have implemented the system with a six-DOF powercube arm and a wrist mounted Hokuyo URG-04LX laser scanner. Our results show that the system is able to autonomously build a 3D model of an object in situ in an unknown environment.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.007
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.002

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.090
GPT teacher head0.346
Teacher spread0.257 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations45
Published2011
Admission routes1
Has abstractyes

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