A fault tolerant state estimation framework with application to UGV navigation in complex terrain
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 a fault tolerant state estimation (FTSE) framework is developed for reliable navigation. The framework features kinematic state estimation using Bayesian filtering of sensor measurements, and sensor fault detection and isolation. Another development is an uncoupled fusion architecture that allows the system state to be updated by asynchronous sensors, makes the system easily scalable and allows the system to degenerate gracefully during one or more sensor outage. A novel procedure to incorporate relative measurements, such as relative pose from stereo sensors, into the Bayesian filtering framework is also developed. In addition, a novel kinematic state transition model is developed that exploits the dynamics of UGV, provides a coupled linear and angular motion model and avoids over-fitting of measurement data. The FTSE system's performance is demonstrated based on results from processing real data.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 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