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
Appearance-based techniques for simultaneous localization and mapping (SLAM) have been highly successful in assisting robot-motion estimation; however, these vision-based technologies have long assumed the use of imaging sensors with a global shutter, which are well suited to the traditional, discrete-time formulation of visual problems. In order to adapt these technologies to use scanning sensors, we propose novel methods for both outlier rejection and batch nonlinear estimation. Traditionally, the SLAM problem has been formulated in a single-privileged coordinate frame, which can become computationally expensive over long distances, particularly when a loop closure requires the adjustment of many pose variables. Recent discrete-time estimators have shown that a completely relative coordinate framework can be used to incrementally find a close approximation of the full maximum-likelihood solution in constant time. In order to use scanning sensors, we propose moving the relative coordinate formulation of SLAM into continuous time by estimating the velocity profile of the robot. We derive the relative formulation of the continuous-time robot trajectory and formulate an estimator using temporal basis functions. A motion-compensated outlier rejection scheme is proposed by using a constant-velocity model for the random sample consensus algorithm. Our experimental results use intensity imagery from a two-axis scanning lidar; due to the sensors’ scanning nature, it behaves similarly to a slow rolling-shutter camera. Both algorithms are validated using a sequence of 6880 lidar frames acquired over a 1.1 km traversal.
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 | 0.002 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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