Why this work is in the frame
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Bibliographic record
Abstract
In this paper we propose a SLAM framework which is based on an algorithm that combines an unscented particle filter (UPF) and unscented Kalman filters (UKFs). A UPF is used to estimate robot's poses and the UKFs are used to represent landmark positions. UPF can estimate robot poses more consistently and accurately than generic particle filters (PFs), especially when models are highly non-linear or noises are not Gaussian. UKF can update landmarks more accurately compared to popular EKF's when highly non-linear observation models are used. In addition, our algorithm avoids the calculation of the Jacobian for both motion model and the observation model, which could be extremely difficult for high order systems. The calculation cost of a UPF is on the same order of magnitude as a particle filter (PF), which uses Kalman filters to generate proposal distributions, and the calculation cost of a UKF is equivalent to an EKF. As a result, our SLAM framework is more accurate than other popular SLAM frameworks while its efficiency is maintained. Simulation results are shown to validate the performance goals.
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 | 0.001 | 0.001 |
| 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