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Record W2069528667 · doi:10.1109/iros.2012.6386271

An autonomous 9-DOF mobile-manipulator system for in situ 3D object modeling

2012· article· en· W2069528667 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicRobotic Path Planning Algorithms
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsMobile manipulatorWorkspaceMobile robotComputer scienceComputer visionObject (grammar)Artificial intelligencePoint cloudTask (project management)Real-time computingSimulationRobotEngineering

Abstract

fetched live from OpenAlex

This video presents an autonomous 9-DOF mobile-manipulator system for 3D modeling of objects in situ. The system consists of a mobile manipulator - a powerbot mobile base with a six degrees of freedom (DOF) powercube arm mounted on it. The arm is equipped with a wrist mounted line-scan range sensor, and the powerbot also has a line scan range sensor mounted on it. The task is to autonomously build 3D model of an object in situ. The system assumes no knowledge of either the object or the rest of the workspace of the robot. The overall planner integrates two next best view (NBV) algorithms, one for modeling and the other for exploration, along with a sensor-based roadmap planner for the manipulator and costmap-based planner for the mobile base. We have implemented the system and this video show that system is able to autonomously build a 3D point cloud model of an object 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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.532
Threshold uncertainty score0.634

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.036
GPT teacher head0.285
Teacher spread0.248 · 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

Quick stats

Citations4
Published2012
Admission routes1
Has abstractyes

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