MétaCan
Menu
Back to cohort
Record W2157777890 · doi:10.1109/robot.2006.1641991

Bearing-only landmark initialization by using SUF with undistorted SIFT features

2006· article· en· W2157777890 on OpenAlex
Xiang Wang, Hong Zhang

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
FieldEngineering
TopicRobotics and Sensor-Based Localization
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsLandmarkInitializationJacobian matrix and determinantExtended Kalman filterComputer scienceScale-invariant feature transformAlgorithmParticle filterFilter (signal processing)Nonlinear systemFeature (linguistics)Bearing (navigation)Kalman filterArtificial intelligenceComputer visionPattern recognition (psychology)Feature extractionMathematicsApplied mathematics

Abstract

fetched live from OpenAlex

In this paper, we present a delayed algorithm for landmark initialization based on a scaled unscented filter. The algorithm efficiently gives well-conditioned feature locations close to the particle filter based methods even for a very high dimensional nonlinear observation model. Comparing with EKF based methods, our method has the same computational cost while the calculation of Jacobian matrix can be avoided. Experimental results are showed to prove the accuracy and efficiency of the algorithm

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)0.000
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: Empirical · Consensus signal: none
Teacher disagreement score0.694
Threshold uncertainty score0.400

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.005
GPT teacher head0.184
Teacher spread0.179 · 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

Citations2
Published2006
Admission routes1
Has abstractyes

Explore more

Same topicRobotics and Sensor-Based LocalizationFrench-language works237,207