An Enhanced Visual-Inertial Navigation System Based on Multi-State Constraint Kalman Filter
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
Over the last few years, the Visual-Inertial navigation systems have attracted considerable attention mainly due to the higher accuracy that is promised by the so-called tightly-coupled scheme where visual and inertial data are integrated at a low level in a common estimation problem. However, the calibration parameters of the camera (e.g. intrinsic and extrinsic parameters) and of the inertial sensor (e.g. sensor's mounting mis-orientation) are often left to be calibrated offline that makes the developed navigation system far from an off-the-shelf product. In this work, an enhanced tightly-coupled Visual-Inertial navigation system, based on the Multi-State Constraint Kalman Filter scheme is proposed that includes the sensors' calibration parameters in the state list to be estimated along with the navigation states. Experimental results on the KITTI odometry dataset shows a considerable improvement in the odometry accuracy compared to the case where those values are obtained from the calibration file of the KITTI dataset.
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