Sensor fusion in mobile robot: some perspectives
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.
Bibliographic record
Abstract
In this paper, techniques and theory work of multiple sensor fusion in mobile robot are reviewed. Mobile robot needs to integrate multiple sensors to accomplish tasks such as map building, object recognition, obstacle avoidance, self-localization and path planning. Our survey describes sensor fusion in three categories: 1) statistically based fusion algorithm policies need the a priori knowledge about the observation process to make inference about identity; 2) neural network and fuzzy set based fusion policies are distribution free and no prior knowledge is needed about the statistical distributions of the classes in the data source in order to apply these methods for fusion; and 3) information theoretic fusion algorithm policies make use of a transformation or mapping between parametric data and a resultant identity declaration. Techniques such as Kalman filtering, rule-based techniques, behavior based algorithms, and approaches range from Bayesian theory, Dempster-Shafer evidence theory to fuzzy logic and neural networks are reviewed. The paper concludes with further research directions.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it